Giving Cows a Digital Voice – AI-Enabled Bioacoustics and Smart Sensing in Precision Livestock Management – A Review

This section discusses how cattle vocal patterns encode biologically meaningful information and the methodological foundations supporting their interpretation.

Understanding cattle vocalizations requires integrating biological, ethological, and acoustic perspectives. Cows are highly social and gregarious animals, and their vocal signals carry rich information about identity, context, and emotional state. As Watts and Stookey noted, vocal behavior in cattle can be viewed as a “subjective commentary” on the animal’s internal condition, with calls conveying age, sex, dominance and reproductive status (Watts and Stookey, 2000; Jung et al., 2021 a). For example, calves emit distinct calls to initiate suckling, whereas adult cows use low-frequency “murmurs” for close-contact interactions and louder high-frequency bellows when alarmed or separated from the herd (De La Torre et al., 2015). Cow’s salivary cortisol levels spiked roughly two-fold in more stressful scenarios as compared to non-harmful states. This is paired along with intensified calling, indicating that vocalizations are often accompanied by physiological signs (Yoshihara et al., 2021). Generally, cows use low-pitched, gentle nasal calls often produced with the mouth closed or partially closed to maintain maternal bonding with their calves (Eriksson et al., 2022). Postpartum dairy cows vocalize in a structured manner around calving and nursing to coordinate with and attract their newborns, reflecting biologically programmed communication patterns that persist across environments (Lenner et al., 2025; Clarke, 2024). On the other hand, when mothers and calves are reunited after period of separation, there is a change in their vocal exchange (Green et al., 2021; Pérez-Torres et al., 2021).

Table 1 lists the main call types alongside their usual frequency range, duration and welfare meaning. Vocalizations are strongly linked to welfare as pain, fear, or frustration typically elicit more intense and frequent calls. On the other hand, positive or low-arousal states (e.g. content grazing or rumination) produce quieter, lower-frequency murmurs (Watts and Stookey, 2000; Meen et al., 2015; Laurijs et al., 2021). Farmers and ethologists have documented cattle modulating vocal signals during estrus and forming social hierarchy or contact with herd mates. Importantly, not all “moos” are similar. Their acoustic structure carries a significant meaning. It is observed that cattle produce at least two broad types of calls that differ in features and context. One type is low-frequency call emitted with a closed or partially open mouth. These are relatively quiet, short distance signals. In contrast, the second type is open mouth call having louder and higher frequency. These are generally used for long-distance communication or in urgent situation. Open-mouth calls tend to occur when a cow is excited, distressed or contacting farther away (Röttgen et al., 2020). It is observed that in intense situations like parturition (calving) cows increased their number of open-mouth vocalizations, whereas during less urgent calf separation they showed more closed mouth or partially opened calls (Green et al., 2020). This indicates that mouth posture and resulting acoustic variation are not random. Indeed, recent work shows that cows produce uniquely individualized contact calls carrying information on calf age (but not sex) (De La Torre et al., 2015), and that separation or handling (e.g. ear-tagging) significantly alters call structure (Schnaider et al., 2022 a, b; Gavojdian et al., 2024). Across breeds and environments, the basic pattern of contact versus distress calls appears conserved, but there is also inter-population variation.

For example, most studies involve European Bos taurus cattle in temperate systems, while Bos indicus or tropical breeds may exhibit distinct vocal characteristics due to anatomical and physiological differences. Bos indicus (e.g., zebu cattle) have been shown to be more reactive to both low and high-frequency sounds compared to Bos taurus, a trait linked to breed-specific differences in auditory sensitivity and head morphology (Gavojdian et al., 2024; Jung et al., 2021 a). Notably, Bos indicus breeds often possess narrower ear canals, more mobile auricles (pinnae), and shorter interaural distances – traits that enhance acoustic localization and responsiveness to a wider frequency range (Moreira et al., 2023). Such structural differences are also associated with variations in cochlear morphology, which can influence both low-frequency hearing and tonal range. These findings suggest that vocal production and perception mechanisms may differ between subspecies, potentially affecting call structure, acoustic salience, and behavioral interpretation across diverse farming systems. Likewise, acoustic profiles differ between open-range and barn environments due to noise and social context. In short, cattle vocalizations are multifunctional signals shaped by evolution and husbandry. They enable mother–offspring bonding, herd cohesion, estrus advertisement, and alarm calls, and they reflect the animal’s physiological state and well-being (Watts and Stookey, 2000).

Taken together, the biological significance of bovine calls is clear: they evolved signals of social and emotional state. However, there remain gaps. Most research focuses on adult cows and calves in specific systems (dairy vs. beef, indoor vs. pasture). Relatively little is known about vocal variation across diverse breeds, climates, and management conditions. Moreover, while calls clearly increase under stress (e.g. during isolation or handling; Watts and Stookey, 2000; Schnaider et al., 2022 a, b), the exact acoustic markers of pain versus other negative states can overlap. Future research must critically examine how genetic background, age, sex, and environment modulate vocal behavior. In essence, the groundwork from behavioral studies has established “what” cattle say, the next step is figuring out “how” to consistently capture and decode those signals across various conditions. Let’s look more into different traditional approaches used for interpreting cattle vocalizations.

Early studies of bovine acoustics relied on behavioral observation and basic audio analysis. Researchers compiled ethograms correlating call types with contexts such as feeding, mating, calf contact, or handling. Researchers are engaged in field observations like watching cows and calves in various scenarios. They manually noted when and how the cow vocalized. These foundational approaches involved manual annotation of calls (e.g. “bellow”, “grunt”, “moo”) and simple spectral measurements, often with custom-built recording setups. These studies correlate the vocalization to specific events or management practices. Watts and Stookey (2000) review these methods, emphasizing that even before advanced technology, ethological observations revealed consistent patterns in cattle vocal behavior (Watts and Stookey, 2000). For example, Jung et al. (2021 a) noted that early studies categorized six distinct cattle call types in mixed herds, with combinations of syllables linked to different social situations.

Along with direct observations, researchers started using technological equipment like audio recorders to document vocalizations for manual spectrographic analysis. They even used simple sensors for continuous monitoring. Observers noted that estrous cows emit characteristic calls, calves emit “ma-ma” contact calls, and pain or fear elicit intense bellows (Jung et al., 2021 a; Nurcholis and Sumaryanti; 2021). An acoustic monitoring system was used to continuously record the soundscape to detect abnormal levels in vocalizations (Alsina-Pagès et al., 2021 a). This approach highlights that it was feasible to collect raw data automatically on farm. But it has some technical limitations like system could record only for short periods and struggled with background noise. Early recordings (using tape recorders and spectrum analyzers) quantified basic parameters like fundamental frequency and duration for these call types. Such studies confirmed that cattle vocalizations can encode age and physiological stress (Watts and Stookey, 2000; Jung et al., 2021 a). The strength of these traditional methods lay in their ecological validity. Calls were labeled in original place, tying acoustic phenomena to rich context. Wearable devices like collar mounted microphones on grazing cows captured vocal events in real time proving that vocalizations can be automatically detected to some extent in complex farm environment (Shorten and Hunter, 2024; Karmiris et al., 2021).

However, traditional approaches have limitations. They were labor-intensive and often subjective, classification relied on human judgment (e.g. Johnsen et al., 2024; Bertelsen and Jensen, 2023; Welk et al., 2024) observers knew which calves were subject to which weaning treatment) and coarse categories. Without automated tools, quantification was limited to a few parameters, and call libraries were small. Moreover, early ethograms did not fully capture the multidimensional nature of sound (e.g. formant structures, harmonics). Figure 3 contrasts the old ‘clipboard-and-stopwatch’ workflow with today’s sensor first loop, highlighting why automation matters. Critically, comparisons across studies were difficult because of varying methods and terminologies. Still, the legacy of these methods is significant, they established the lexicon of cattle vocal communication and provided initial evidence that vocal behavior reflects biological state (Watts and Stookey, 2000). This historical record forms a baseline for modern quantitative analysis. The foundational call catalogues (e.g. by Kiley, Watts) remain essential, but modern research must reinterpret them with quantitative tools.

Figure 3.

Comparison of a traditional, human-centric welfare assessment workflow (left) with an AI-enhanced, sensor-driven loop (right). The manual pathway relies on periodic visual scoring and can delay intervention by days, whereas the smart pathway fuses continuous acoustic, motion and video data, runs edge AI for instant anomaly detection, and provides interpretable alerts that prompt rapid farmer action

Contemporary analysis dissects cattle calls into detailed acoustic parameters. For cattle vocalizations, commonly measured acoustic parameters include fundamental frequency F0, the range and variation of frequencies (including high harmonics and formants), the duration and temporal pattern of the call and the amplitude or loudness profile. By plotting the calls on spectrograms, we can visually inspect these characteristics and identify patterns that might not be recognized by human ear. Signal processing techniques like Fourier analysis and Mel-frequency cepstral coefficients (MFCCs) are used to generate these spectrograms and calculate the frequency domains. F0 is a primary metric, it relates to the source (vocal fold vibration) and often correlates with arousal or call intensity. For example, De La Torre et al. (2015) found LFCs with mean F0 ≈81 Hz and HFCs ≈153 Hz, reflecting tension in the sound source. Other time domain measures include call duration, amplitude envelope, and call rate. Temporal patterns (e.g. repetition rate of moos) also encode information (e.g. frantic short calls during pain vs. long low calls in estrus).

In the frequency domain, researchers extract spectral features. Formant frequencies (resonances shaped by the vocal tract) can signal age or size: adults have lower formants than calves (De La Torre et al., 2015; Brady et al., 2022). Spectral energy distribution (e.g. spectral centroid, bandwidth) can differentiate call types (bellows are broad band, while murmurs are narrow). A powerful set of features comes from cepstral analysis, MFCCs and related cepstral descriptors capture the timbre of calls. These are now standard inputs for machine learning models. For instance, Jung et al. (2021 b) combined MFCCs with a convolutional neural network, achieving ∼91% accuracy in cattle call classification. Table 1 summarizes detailed acoustic profiles of various bovine vocalization types, correlating physical acoustic parameters with specific behavioral and welfare implications. Cattle vocalizations occupy distinct frequency bands and patterns as compared to other sounds like the whirr of a machine or a bird chirp. This makes it possible to algorithmically detect cow calls from the noise. By comparing time domain waveforms and frequency spectra we can filter out non cattle sounds and isolate the cow calls (Özmen et al., 2022).

In practice, decomposition often uses tools like spectrograms and glottal flow estimations. Modern software (e.g. Praat, Raven) computes these parameters efficiently. Yet interpretation requires care, acoustic features are influenced by factors like head posture, background noise, and recording equipment. Comparative studies have shown, that pain vocalizations tend to have higher F0 and more chaotic spectra than calm calls (Schnaider et al., 2022 b). Moreover, cross-validating features against physiology is critical. For instance, sharp changes in pitch or amplitude during handling correlate with elevated cortisol. The raw to alert journey sketched later in Figure 4 starts with the very spectrogram slices we describe here.

Figure 4.

Illustration of an AI-driven acoustic analysis pipeline for decoding bovine vocalizations, from audio acquisition through preprocessing and modeling to real-time farm alerts

Ultimately, acoustic analysis provides a quantitative “fingerprint” of each call. When linked with behavior, it allows calls to be labeled as “distress” or “content” with statistical confidence. However, there is no single acoustic marker of welfare, rather, patterns across multiple parameters are informative. For example, a low F0, long-duration “murmur” might indicate relaxed ruminating, whereas a high F0, broadband roar signals acute stress (Meen et al., 2015; De La Torre et al., 2015). Integrating these signals through algorithms and classifiers can automate interpretation.

Yet purely acoustic studies have their own limitations. We can say that a particular cow call might measure a high pitch and certain duration, acoustic analysis might fit this call to either estrus or mild distress call. Without having contextual information, distinguishing between these two calls becomes difficult. While advanced feature extraction like using a broad spectrum of MFCCs or nonlinear acoustics to detect softness improves detection and classification, it becomes clearer and explainable when the context is applied.

Both breed and environment significantly influence the acoustics of cattle vocalizations. Genetic differences between breeds (e.g. Bos indicus vs. Bos taurus) can lead to shifts in call frequency, structure, and clarity. For instance, Bos indicus (zebu) cattle have been found more reactive to very low and high frequency sounds than Bos taurus, a difference attributed to their distinct ear anatomy and hearing sensitivity (Moreira et al., 2023). Such anatomical variation (including vocal tract length and auricle shape) may translate into subtle differences in vocal outputs for e.g. potential formant frequency shifts or different F0 ranges across breeds (Burnham, 2023). Indeed, vocalizations of larger or anatomically different breeds often exhibit lower resonant frequencies (formants) than those of smaller breeds or related species. Comparative studies support this, the mean pitch of calls by water buffalo cows was shown to differ significantly from that of European grey cattle, highlighting how genetics impact vocal frequency (p<10⁻¹³) (Lenner et al., 2025). However, not all breed effects are large, individual variation can sometimes outweigh breed averages (Lenner et al., 2025), so robust models must account for intra- as well as inter-breed variability.

Environmental context further modulates call acoustics. An indoor barn vs. an open pasture presents very different sound propagation conditions. In reverberant enclosed spaces like barns, low frequency, narrowband calls can resonate and carry further (echoing off walls), whereas higher frequency elements may become attenuated or blurred (Burnham, 2023). The acoustic adaptation hypothesis (AAH) suggests that animals adjust their call structure to the habitat, calls in “closed” environments (forests or barns) tend to use lower frequencies or prolonged tonal elements to maximize transmission, while calls in open environments can afford higher frequencies and rapid frequency modulation (Burnham, 2023). Cattle are no exception – a call recorded in a concrete barn may have a muddier sound (longer decay, overlapping echoes) compared to the same call outdoors. Studies have had to consider such factors when generalizing models, like analysis by Gavojdian et al. (2024) noted that prior datasets included vocalizations from both pasture mobs and barn settings. The farm environment can thus introduce acoustic distortion (e.g. reverberation, background machinery noise) that affects call clarity and measured features. This impacts cross-farm generalization, a classifier trained on clean pasture recordings might falter on barn recordings (and vice versa) if not designed with noise robust features. Addressing breed and environment variation, e.g. by normalizing for formant differences and augmenting data with reverberation effects is crucial for developing models that perform reliably across herds and farms (Gavojdian et al., 2024; Burnham, 2023).

As seen in the limitations of acoustic analysis, it improves the ability to detect and characterize cattle vocalizations, understanding the meaning of these sounds requires additional context. Consequently, recent research has made towards multimodal data integration combining vocalization data with other sources of information. A cow’s vocal behavior does not occur just in isolation, but it is linked with her physical actions, physiological state, and the surrounding environment. The semiotic repertoire concept suggests that the cattle communicate through multiple channels (like auditory, visual, olfactory) (Cornips, 2024). And these signals together convey the animal’s intent and condition. For example, a cow might bellow loudly and pace restlessly when separated from her calf. This indicates distress, but the addition of her movement pattern confirms the level of agitation. On the contrary, a cow might vocalize with a similar sounding call in two different situations when she is alone, or she is in crowded pen at feeding time. Thus, only by noting the context we can interpret the call correctly.

Modern precision livestock systems therefore combine vocal data with other sensors. For instance, video cameras or 3D tracking provide information on posture and social context, accelerometers reveal activities (grazing, walking, lying), environmental sensors record temperature or humidity, while health monitors track physiology (Arablouei et al., 2024; Kok et al., 2023). By synchronizing these modalities, we can interpret calls more accurately. For example, an open-mouth call recorded while a cow is isolated from the herd (monitored by positioning sensors) is more likely distress than if the cow were simply feeding and grazing. A study on multi-modal datasets (MMCOWS) illustrates this approach, it collected synchronized audio, inertial (motion), location (UWB), temperature, and video data from dairy cows, yielding millions of annotated observations (Vu et al., 2024). Such datasets enable algorithms to cross-validate vocal cues, if audio detects a call, the system checks cameras and accelerometers to confirm whether the animal is running (panic) or ruminating quietly.

Research has begun to exploit these synergies. Wearable collars combining microphones and accelerometers can, for example, distinguish coughs from calls by correlating sound with breathing motions. Farms increasingly deploy acoustic sensors alongside cameras in barns to flag abnormal behavior, a cow calling persistently at the water trough (audio) while isolated (camera) can prompt an alert (Lardy et al., 2022; Stachowicz et al., 2022). These multi-modal systems tend to improve classification accuracy. In pilot by Wang et al. (2023 a, b), combining audio with eartag RFID data and motion sensors allowed machine learning models to identify feeding versus social vocalizations with far higher precision than audio alone. For instance, a dual-channel audio recorder attached to a cow can capture sound from two microphones simultaneously (one oriented toward the cow’s throat and other to the environment), allowing software to differentiate the cow’s own vocalizations from background noise. Beyond audio, a complete multimodal system includes video cameras watching the herd, accelerometers on cows measuring their movement, GPS units logging their location, and even physiological sensors (like heart rate or rumen sensors). Figure 7 pulls all those data streams together in one schematic, showing how sound, motion and video converge on a single dashboard. Such prototypes have improved the ability for behavior recognition.

Cattle behavior classification system used an improved EdgeNeXt, a lightweight edge CNN, to fuse data from multiple inertial sensors, turning motion signals into images for analysis (Peng et al., 2024). It combines images with spectrogram patches, achieving 95% accuracy in classifying social licking vs. rumination. But incorporation of additional modalities such as video or audio could further enhance model performance. Table 2 summarizes which extra sensors pairs well with barn audio and what each combination can flag in real time. Few studies have fully integrated audio with a wide array of other sensors specifically for decoding cattle calls, which marks a clear direction for future research.

Despite their promise, multimodal approaches present challenges. Challenges include synchronizing data streams (ensuring that the moo and the movement data align in time) and handling the volume and complexity of data that such systems produce, deploying numerous sensors on a farm can be expensive and technically demanding. It is observed that multimodality reduces ambiguity by confirming a vocalization context through other evidence reducing false positives and false negatives. There are also trade-offs in farm deployment (cost, data bandwidth). Nonetheless, the trend is clear, to “give cows a digital voice” we must listen not only with microphones but with a network of intelligent sensors.

Models that perform well in one environment “Farm A” may not generalize to other “Farm B” having noise and different acoustic conditions. Traditional classifiers like SVM, k-NN, Naïve Bayes, Random Forest, Hidden Markov Models start with hand designed descriptors (pitch, formants, call duration, energy) and then learn decision boundaries. When those features capture the “essence” of a task, accuracy can be strikingly high. An SVM outperformed all peers in detecting estrus vocalizations (Sharma and Kadyan, 2023), and a Random Forest reached ∼93% F1 for predicting metritis from combined vocal behavioral cues (Vidal et al., 2023). k-NN and RF readily separated high arousal open-mouthed isolation calls from low-arousal closed-mouthed feeding calls in Japanese Black cattle, peaking at 96% accuracy (Peng et al., 2023). Even in noisy shed conditions, a collar microphone plus supervised learning still distinguished “any moo” from ambient clatter with >99% accuracy (Shorten, 2023). However, these approaches offer a relatively straightforward pipeline (feature extraction followed by classification) that has provided preliminary insights into cow communication capabilities.

Despite these successes, a fundamental challenge is that cow calls are acoustically complex signals that can vary with context, individual, and environment, where simpler models struggle to capture the intricate variability present in cow vocal communication. Moreover, many algorithms perform well only after extensive feature engineering, which requires domain expert knowledge and can be biased. A few averaged MFCCs flatten the evolving frequency contour of a pregnant moan, and HMMs only add temporal context if researchers build extra states. Most machine learning studies in cattle vocalization rely on small datasets, typically involving fewer than 20 animals from a single herd. This limited scale increases the risk of overfitting and restricts the ability of models to generalize across environments and breeds. Among the reviewed literature, only one study – Gavojdian et al. (2024) – released a curated dataset involving more than 1,000 labeled vocalizations. The dataset, known as BovineTalk, includes 1,144 manually annotated calls recorded from 20 dairy cows exposed to visual social isolation. This remains the largest openly available supervised learning dataset in the field. In contrast, most other studies included fewer than 1,000 samples and involved smaller, homogeneous groups, which limits scalability. The SVM that excelled on its home herd (Sharma and Kadyan, 2023) stumbled on a second farm with different acoustics, age mix, and management style, confirming worries about brittle performance (Peng et al., 2023; Vidal et al., 2023). These handcrafted models also show their limits when extra streams join the mix.

Another major constraint is noise. The noise robust foraging detection NRFAR pipeline filtered out machinery buzz and retained 86% accuracy in moderate noise, but performance slid once tractors backfired or multiple cows overlapped (Martinez-Rau et al., 2025). Class confusion also arises, the RF from Peng et al. (2023) occasionally mislabeled excited social calls as mild distress because their spectral envelopes overlapped. Without a public benchmark or cross-farm validation, gaps repeatedly flagged by researchers, cannot tell whether a reported 95% is a true breakthrough or a fragment of easy data.

Despite these caveats, supervised models remain valuable:

• They are computationally light, suiting edge devices,

• Their feature weights or decision trees are interpretable,

• And they set baseline expectations for deeper networks.

Hand-built features and small datasets let classical ML reach eye-catching numbers, yet the moment we change barns, breeds, or background noise, the model performance deteriorates.

Although a neural network may detect subtle sounds beyond human perception, its reliability becomes uncertain in conditions of limited data, high background noise within the barn, and an opaque decision-making process.

Deep learning has transformed bioacoustics by letting models learn directly from raw waveforms or Mel-spectrograms, bypassing handcrafted features. “DeepSound”, a CNN–LSTM stack, extracted such patterns from calf distress calls and reached nearly 80% macro-F1 despite minimal feature engineering (Ferrero et al., 2023). A separate CNN with seven convolutional blocks classified cow “intent” calls (hunger, stress, estrus) at ∼97% accuracy (Patil et al., 2024), showing how spectral differences map to different motivational states of the animals.

When the acoustic environment of a barn differs from that of the research facility – such as having louder ventilation systems or older stall designs – the performance of a recently developed state-of-the-art model may be compromised, potentially leading to reduced reliability and increased risk of misinterpretation.

Noise, overlap, and the “false-alarm tax”

Barns are chaotic soundscapes machinery drones, calves overlap calls. The noise robust NRFAR pipeline kept recall above 0.80 under moderate tractor noise (Martinez-Rau et al., 2025), but false positives doubled in full-milking rush hour. A common observation is that models tend to produce more false positives in noisy conditions, by interpreting random noises as cow calls or misidentifying one cow’s call as another’s. Conversely, false negatives occur when a cow’s call is drowned out by background noise or overlaps with another sound. The study using overlapping time windows (Vidana-Vila et al., 2023) for detection clearly demonstrated this tradeoff, where setting a sensitive threshold caught all the true vocalizations but at the cost of false alarms, whereas a stricter threshold missed some quieter calls. Figure 5 illustrates the noise adaptation loop we lean on whenever tractors or fans drown out the calls. Our noise tests underscore why the converged layout in Figure 7 routes audio, video and THI into a single classifier, no one stream stays clean for long in a working barn.

Figure 5.

Noise adaptation pipeline used in our review’s NRFAR style studies. Raw barn audio first undergoes spectral gating and bandpass filtering, then an adaptive denoiser whose coefficients are fine-tuned on site-specific noise samples. A log-Mel feature bank feeds a noise-aware CNN that outputs both class and confidence; low-confidence events trigger a feedback loop that stores new noise exemplars and refreshes denoiser parameters, maintaining robustness without full model retraining

When an AI model interprets a sharp, high-pitched moo as an indication of pain, are we genuinely capturing the cow’s own communicative intent, or are we merely imposing human concepts of language and emotion onto its vocalizations?

Animal bioacoustics has increasingly borrowed tools from human speech processing. Early methods extracted engineered features from recordings. For example, MFCCs and spectrograms, which compress the raw sound into measurable patterns (like frequency bands, durations, and amplitudes). However, modern deep learning studies find that such handcrafted features often under-perform compared to using minimally processed inputs. MFCCs, while once popular, tend to be a poor match for convolutional networks and are often outperformed by mel-spectrograms or even raw waveform inputs (Stowell, 2022). In practice, researchers now often feed spectrogram images or learnable filter banks directly into neural networks, or apply transformer architectures (e.g. WaveNet, Temporal Convolutional Networks) to raw audio, letting the model discover the best features (Stowell, 2022). Cattle vocalizations can be converted into textual representation to perform sentiment analysis, labeling calls as conveying positive (calm/content) or negative (distressed) affect. It uses OpenAI Whisper model (originally designed for human speech) to transcribe dairy cow moos into text (Jobarteh et al., 2024). Crucially, this NLP driven pipeline was combined with traditional acoustic feature analysis, creating a multi-modal fusion of linguistic and sound features. It achieved over 98% accuracy in distinguishing distress vs. calm calls.

Beyond these acoustic representations, NLP techniques bring a new dimension. Large pre-trained models and self-supervised frameworks such as Facebook’s Wav2Vec2.0, HuBERT, or custom animal-audio models learn from vast unlabeled audio to capture general sound structures. For example, the AVES (animal vocalization encoder based on self-supervision) model is a transformer trained on diverse unlabeled animal recordings. AVES learns rich acoustic embeddings without explicit labels, and when fine-tuned on specific tasks (like cow call classification) it outperforms fully supervised baselines (Hagiwara, 2023). In other words, models like AVES and Wav2Vec leverage huge unannotated data-sets to bootstrap learning in data-poor domains like cattle calls. These models are often open source; for example, the AVES weights have been released publicly (Hagiwara, 2023), enabling researchers to adapt them to livestock sounds. This is especially important in bovine bioacoustics, where gathering large, labeled datasets is challenging, thus LLM-inspired models offer a way to build abundant raw audio to meaningful understanding.

Another NLP-inspired strategy is to convert vocalizations into a textual or symbolic form and then apply language-model techniques. For instance, OpenAI’s Whisper, an encoder-decoder ASR system trained on 680,000+ hours of human speech (Radford et al., 2023) has been repurposed to process animal sounds. In a recent study, chicken vocalizations were passed through Whisper to produce “text-like” tokens and sentiment scores. Although these outputs were not literal chicken language, the resulting token patterns and sentiment scores tracked welfare states, stress conditions yielded markedly different token sequences and higher “negative” sentiment than calm conditions (Neethirajan, 2025). This suggests that even when a speech model is trained on humans, its robustness (to noise, accents, and domain variation) can still capture salient acoustic cues in animal calls (Radford et al., 2023; Neethirajan, 2025). Similarly, audio models like Whisper or Wav2Vec2 can serve as front ends, they transform raw sound into intermediate representations (either symbolic or vector) that feed into downstream classifiers. For cows, one pipeline could first use an ASR to get a transcript-like sequence and then use an LLM to infer the meaning or emotion from that sequence. This mirrors human voice assistants, they transcribe speech to text and then have an LLM (e.g. GPT) answer questions about it. Indeed, benchmarks in human speech show that a modular pipeline (ASR + LLM) can outperform end-to-end audio models on understanding commands. The language models we test simply plug into the back end of the audio pipeline in Figure 4, so no extra sensors are needed to try them out.

By analogy, a “cow-voice assistant” could use an ASR (trained or adapted to cow sounds) followed by an LLM prompt to interpret a call (e.g. “alarm call during milking”) and output a human friendly explanation (like “cow appears anxious: possibly isolated calf”). Multi-modal approaches extend this further. Vocal data can be fused with contextual cues (video of behavior, environmental sensors, physiological data) to improve interpretation. For instance, study by Jobarteh et al. (2024) used a custom ontology to fuse acoustic features with NLP-transcribed tokens, categorizing high-frequency cow calls as “distress” and low-frequency calls as “calm”, yielding robust welfare inferences. In practice, combining sound with sensor data (movement, temperature, herd activity) creates richer context, allowing an NLP module to reason in a more humanlike way about the animal’s situation.

Thus, NLP methods enable us to handle unstructured audio in new ways. Self-supervised audio transformers (e.g. Wav2Vec2, AVES) can learn from unlabeled farm recordings, dramatically reducing the need for expensive annotations. Converting sounds into “text” via ASR unlocks powerful LLM reasoning (as in Whisper and AudioGPT architectures), but we must be mindful that these models were not trained on cows, so domain mismatch is a major caution. Early speech-inspired features laid the groundwork, but the current trend is toward end-to-end learned representations that can directly capture the complexity of animal sounds.

Ultimately, the goal is to turn a cow’s moo into a meaning that farmers can understand. In practice, this means mapping acoustic patterns to welfare indicators or behavioral states (not literally “words”). Several studies have tackled this by training classifiers on annotated calls. For example, system automatically detected lameness in dairy cows by classifying their grunts; cows with a limp produced recognizably different acoustic signatures, and a model mapped these signatures to a diagnosis of “likely lame” vs “healthy”. Another project monitored calf calls in a barn, by detecting when calves vocalize and how urgently, the system inferred contexts like hunger or separation. When calf calling spiked, the AI alerted staff that “calf calling has increased possible isolation stress”. These applications show the idea of a “translation”, turning raw sounds into diagnostic or semantic labels that people can act on (Ntalampiras et al., 2020; Jobarteh et al., 2024).

Some efforts have even built preliminary “ontologies” of vocalizations. In recent study by Jobarteh et al. (2024), researchers manually clustered cow moos by their acoustic shape and associated context, for instance, they defined a class of high-pitched, harsh moos linked to agitation, and a class of low, steady moos linked to contentment. Once such categories are defined, machine learning can classify new recordings into these categories with high accuracy. Large language models can then generate a simple explanation. For example, after a bioacoustics classifier labels a call as “low-pitched, calm call of a mother”, an LLM can output a sentence like “This sound suggests a cow calmly calling her calf”. In this way, the pipeline converts a vocal event into textual advice or insight, essentially giving the cow a “voice” that humans can interpret. Every semantic tag we generate is anchored in the baseline call types set out in Table 1, so the LLM never freewheels beyond known behavioral contexts.

However, it is crucial to remember that this is a form of inference, not a literal translation. So far, the mappings are at best coarse, calls are labeled by affect (e.g. calm vs distressed) or broad functions (e.g. feeding, social contact, panic). Unlike human speech, animal calls do not have a known lexicon of words. Instead, they carry statistical cues about internal state or immediate context. For example, an isolated calf might emit a distinctive contact call but that call is not guaranteed to mean “I am alone” in the way a word would. It simply correlates with the situation. Studies in other species show that animals do produce acoustically distinct alarm or contact calls that reliably predict a response in listeners, but these signals evolved to influence receiver behavior rather than to convey abstract meanings (Seyfarth and Cheney, 2003). In practice, we rely on human observation to label what a cow probably means (e.g. fatigue, fear, hunger) and train the model accordingly. Some work even uses topic-model techniques (like latent Dirichlet allocation LDA) on transcribed call tokens to find latent “topics”. For instance, a study of poultry Whisper output applied LDA and found clusters of token sequences that neatly separated stressed from unstressed calls (Neethirajan, 2025). This unsupervised approach discovered recurring “themes” in the data without pre-defined labels, akin to a taxonomy of call types. Figure 6 shows an NLP enabled workflows translating acoustic features of bovine vocalizations into interpretable textual descriptions. (e.g., converting distress calls to ‘I need help’).

Figure 6.

NLP and LLM approaches to “cow language” translation

Figure 7.

Schematic model for multimodal data integration (audio, movement, video) in precision livestock farming, emphasizing how sensor fusion yields context-rich insights into animal health and stress

Comparing approaches highlights the old vs. new, classical taxonomic models like LDA or k-means can group calls into clusters, but they lack the rich semantics of pre-trained models. By contrast, foundation models (audio or text based) offer deep representations that might capture subtle patterns across contexts. For example, an LLM or audio transformer could represent a call by its emotional tone, rhythm, and spectral features simultaneously, potentially linking it to learned concepts from human language. Open source frameworks are emerging too, researchers have begun fine-tuning speech transformers on animal data or using multi-modal fusion to jointly model text and sound (Jobarteh et al., 2024).

Thus, translating calls is more about classification than literal language. Current systems label moos with welfare relevant tags (pain, hunger, calmness) and then summarize them in plain text, but this “dictionary” is human constructed and far from complete. Calls often have graded acoustic variation rather than discrete units, models may group them by context (using topic models or embeddings) rather than by fixed “words”. In essence, we interpret calls as “messages” about need or affect, but always as probabilistic signals inferred from context. Building larger, multi-herd datasets and richer ontologies will help, but semantic ambiguity will remain a core challenge. Table 5 shows comparison of state-of-the-art NLP and speech models for animal bioacoustics, outlining inputs, purposes, and trade-offs.

A significant hurdle in decoding animal vocalizations is the limited availability of labeled training data. It is infeasible and expensive to gather thousands of examples of every type of cow call along with ground truth annotations of what they mean.

Few-shot and zero-shot methods aim to sidestep this. These approaches allow AI models to generalize from very few examples by relying on prior knowledge learned from other data. In the context of bovine acoustics, using few-shot learning means a system could learn to recognize a new kind of vocal signal from only a handful of labeled instances. In bioacoustics, few-shot systems have shown promising results: prototype-based networks with channel-spatial attention achieved F1 scores ∼67% for bird vocal detection despite minimal labeled examples (Liu et al., 2023). Similarly, self-supervised pre-training on unlabeled bird sound databases improved transferability to unseen species (Moummad et al., 2024). In the DCASE few-shot challenge, meta-learning and contrastive approaches consistently achieved 60–70% F-scores in animal sound event detection (Liu et al., 2024 b). For zero-shot learning, audio language models such as CLAP successfully recognized a variety of animal sound categories including birds, frogs, and whales without additional fine-tuning (Miao et al., 2025). While these developments affirm the potential of few/zero-shot methods, their transferability to cattle acoustics characterized by noisy farm environments and subtler vocal distinctions remains to be empirically validated. Study by Nolasco et al. (2023) showed that feeding only five labeled examples of a new cow vocalization into a pretrained network allowed accurate detection of that call in continuous audio. The aforementioned AVES model exemplifies this, after self-supervised pretraining on extensive unlabeled animal recordings, AVES can be fine-tuned with very little cattle data and still achieve high accuracy (Hagiwara, 2023). Essentially, any new animal call classification task can start with AVES’s original feature set i.e. “prior knowledge” and require only a tiny fraction of data for training compared to starting from scratch.

Language models offer another twist on data efficiency. Systems like AudioGPT (and related frameworks) use LLMs as high-level controllers to orchestrate specialized modules (Robinson et al., 2024; Rubenstein et al., 2023). AudioGPT for example complements ChatGPT with pre-trained audio encoders and ASR/TTS interfaces (Huang et al., 2024). In principle, one could ask such a system: “Detect if any cow is in distress”. The LLM would then call on an off-the-shelf cow-call detector and emotion classifier (none of which were trained on that exact query), combine their outputs, and answer the question. This kind of zero-shot chaining means we can tackle new inference tasks without retraining models end-to-end for each task. Similarly, a model trained to recognize “distress” sounds in one species might detect analogous distress cues in cows, even without cow-specific training, if arousal-related acoustics overlap across species. (Indeed, cross-species studies suggest that certain vocal indicators of negative emotion like a raised fundamental frequency appear broadly in mammal (Seyfarth and Cheney, 2003)). Certain acoustic characteristics of arousal or pain are conserved across species, suggesting a well-trained model might detect signs of agitation in cow calls even if it was trained on a broader bioacoustic dataset not specific to cows (Lefèvre et al., 2025).

Generative models further bolster these low-data strategies. Neural audio synthesis (using GANs, diffusion models, or autoregressive “neural vocoders”) can create synthetic cow calls to augment training sets (Pallottino et al., 2025). For a rare alarm call, an AI could learn its acoustic pattern and generate many realistic variants. Early work in birdsong and marine mammal bioacoustics has shown that such synthetic augmentation can improve classifier robustness. Some of the studies used controlled sound generation techniques to model animal calls, generating simulated examples that help explore and validate classification methods (Hagiwara et al., 2022). Such synthetic data approaches, combined with few-shot learning algorithms, form a powerful toolkit. Thus, rather than collecting extensive new datasets each time, few-shot capable models let us quickly calibrate to these conditions on unseen vocal behaviors. In the bovine context, generative models might simulate how a calf’s bleat sounds under extreme hunger or stress, providing novel examples that are hard to capture in real life. However, care is needed, if synthetic examples are not truly representative, they could mislead the classifier. Every few-shot or synthetic inference must still be validated with real data and ethological expertise.

Data efficiency tricks are indispensable in livestock bioacoustics. Self-supervision (AVES, Wav2Vec) and transfer learning let us exploit existing audio or language models so that only a few bovine examples are needed to build a useful detector. Zero-shot compositions (with LLMs directing ASR, etc.) promise flexible question-answering about animal sounds. Generative augmentation offers another path to “new” data. Yet all these methods demand caution, few-shot models can be overconfident, and zero-shot guesses can be wrong if human priors are incorrect. Rigorous testing (and human-in-the-loop checks) is essential to ensure that a model fine-tuned on minimal examples truly generalizes to new herds and settings.

Applying NLP tools to cattle calls involves both practical and philosophical hurdles. On the technical side, recording quality and environmental noise are major issues. Farm audio is often noisy, machinery, other animals, wind, and crowd vocalizations overlap. Models trained on clean, close-range recordings may fail in a barn. Before we worry about bias, we have to survive the barn’s acoustic chaos, hence the noise-adaptation loop implemented in Figure 5. Researchers are tackling this with robust preprocessing: for example, “bio-denoising” networks have been developed that can clean noisy animal calls without requiring a clean reference (Miron et al., 2025). Data scarcity and variability are also critical. Cows of different breeds, ages, or individual personalities vocalize differently, and even the same cow varies its calls by context. A dataset collected on one farm might not represent another. To improve generalization, scientists expand training sets across farms, use data augmentation (pitch/time shifts, background mixing), and rely on the self-supervised pretraining mentioned above (Hagiwara, 2023). In practice, multimodal fusion mitigates many issues, combining sound with video tracking or accelerometer data can disambiguate a call’s meaning. For instance, a cow vocalizing while lurching could be interpreted differently from a cow vocalizing during feeding. Indeed, integrating audio with movement and temperature sensors is emerging as a best practice for accurate interpretation (Chelotti et al., 2024).

Ethically, the foremost concern is misinterpretation. A model that falsely dismisses a real distress call (false negative) or falsely alarms on normal behavior (false positive) can erode trust. For any critical application (health monitoring, welfare alerts), it is important that a human verify the model’s output. Closely related is the risk of anthropomorphism. Labeling cow calls with human emotions (“happy”, “frustrated”) or intent implies a level of understanding we do not have (Seyfarth and Cheney, 2003; Watts and Stookey, 2000). At best, such terms are convenient proxies; at worst, they may mislead caretakers into thinking the AI sees more than it does. Ethical use requires that we present predictions as probabilistic indicators (e.g. “possible hunger signal”) rather than factual translations.

On privacy and data issues, livestock voices themselves are not protected personal data. However, farm managers and workers should consent to recordings and be aware that audio data can indirectly reveal sensitive information (such as an outbreak of illness by elevated coughing). Transparency with stakeholders about data use is important. More broadly, the goal of this technology is to help animals. For instance, by alerting farmers to pain or stress earlier than might be noticed. We must ensure these AI tools are used to improve welfare not to justify neglect. This ethical framing means constantly evaluating: Do cows vocalize more as a cry for help than a guarantee of help? Is a detected “stress call” actually leading to timely intervention? Building systems with veterinarians, ethologists, and farmers is crucial to keep the technology grounded.

Using human language techniques on animals inevitably raises philosophical questions. A key debate is whether we are projecting human concepts onto fundamentally different communication systems. Critics warn that terms like “dog saying hello” or calling a chicken’s cluck “excited” are anthropomorphic shortcuts that may misrepresent animal experience. In scientific terms, cows likely do not use language with syntax or symbolic semantics. Rather, as ethologists note, vocalizations serve to influence listeners and reflect the caller’s state, not to share an explicit message with intent (Seyfarth and Cheney, 2003; Watts and Stookey, 2000). For example, a cow’s alarm grunt might occur because she is frightened listeners hear this and may infer “danger” but the cow is not “saying the word ‘danger’”. She is producing an arousal-driven call. Listeners (including AI models) then pick up information correlating with context. In short, cows broadcast their condition (“I’m agitated!”) more than they communicate in the human sense of intentional messaging (Seyfarth and Cheney, 2003; Watts and Stookey, 2000).

Philosophically, this limits how much we can “translate”. Human language involves shared symbols and often complex syntax. Animal signals, by contrast, tend to be graded and multi-dimensional. A cow’s vocal repertoire may blend elements of urgency, pitch, duration in ways that do not neatly segment into words (Cornips, 2024). Any attempt to label these by human feelings (happy/sad) or discrete semantic units is necessarily an approximation. For instance, labeling a call as “pain” assumes a one-to-one mapping, but in reality, a painful experience might produce a range of call variants. Studies caution against reifying these labels. We should remember that our so-called “translations” are inferences. This aptly captures the difference, the cow’s moo reflects her physiology and emotions, but she is not conversing in a language.

Empirically, we lack ground truth semantics for cow calls. Unlike human speech datasets (which have transcripts and meanings), cattle datasets rarely have verified meanings beyond the context (e.g. recorded after a stimulus or during a known condition). This means any model output is unverified interpretation. It is possible to statistically validate that certain calls predict certain outcomes (say, high-arched moo precedes feed delivery), but even then, we may not understand why the sound has that shape. The absence of a “cow dictionary” means our AI pipelines must remain humble. They can suggest alerts or categories, but they cannot claim absolute understanding.

In weighing functional versus affective interpretations, modern consensus is that animal calls often convey both, but primarily through correlations rather than explicit semantic (Seyfarth and Cheney, 2003). A single grunt could function as an alert (a referential role) and also indicate fear (an affective overlay). In cattle, such grunts are typically produced nasally, with the mouth closed or partially closed, and are associated with low-frequency communication over short distances. Our models usually focus on affective classification (e.g., distress vs calm), as these states are more reliably observed. Contrastingly, assigning specific referential meaning (like “there is a fox”) is generally unsupported except in a few well-studied species. For cows, we have no evidence of such referential calls. Thus, when an AI says a cow is “crying for her calf” or “suffering”, it is drawing on human analogies to emotions, not decoding a literal statement from the cow. This is not necessarily wrong (cows do separate-calm versus isolation-distress), but it is a guess that requires validation with behavior or physiology. Ultimately, the anthropomorphism risk reminds us to interpret AI outputs with care. The utility of these systems lies in pattern recognition, not in bridging a mysterious language gap. If an NLP model tells us “Cow likely in pain”, we should verify with physical signs (lameness, vitals) rather than assume the AI speaks cow truth. In other words, a robust result was consistent change in the model’s token stream, not a decipherable message.

Imagine a barn capable of signaling the moment a cow experiences discomfort; while early AI alerts could enhance the quality of care, there may also be concerns that continuous digital monitoring adds complexity and additional data burdens to the management of precision livestock farming.

Modern farms are piloting AI-driven vocalization monitors to capture subtle health and reproductive signals. Precision livestock farming (PLF) is increasingly adopting AI tools to continuously monitor individual animals and make farm management easier. A review noted that health and disease detection are most common targets for machine learning in dairy farming (Slob et al., 2021). Parallel advancements in deep learning applied to cattle imagery, including AI-powered cow detection in complex farm environments, further illustrate AI’s growing role in herd management (Mahmud et al., 2021; Araújo et al., 2025; Rohan et al., 2024). In the bioacoustics domain, studies have shown that collar mounted acoustic sensors can automatically monitor feeding behavior and classify chewing and rumination from sound signals using machine learning (Abdanan Mehdizadeh et al., 2023; Chelotti et al., 2020). Modern farms are piloting AI-driven vocalization monitors to capture subtle health and reproductive signals (Baig and Shastry, 2022; Silva et al., 2024). For example, wearable collars equipped with microphones and motion sensors now perform onboard analysis of each cow’s behavior. A recent system used a collar containing a micro-processor, accelerometers and an LPWAN radio to process feeding and activity patterns locally (Martinez-Rau et al., 2023 b, 2024). Every hour it computes how much time the cow spent grazing, ruminating, or resting, and transmits a concise summary to the farmer. If a cow’s eating rate or total chewing time drops below normal, the system can promptly alert farmers stating a potential early sign of illness or discomfort that might otherwise go unnoticed until the cow’s condition worsens.

AI-based analysis of vocalizations is also being applied to broader behavioral and physiological indicators. ML models can classify different types of cattle calls that correspond to important states. The system can distinguished food-anticipation calls, estrus calls, coughs, and normal mooing with over 80% accuracy (Lefèvre et al., 2025). This means the farm will receive a notification that a particular cow is likely in heat or that a cow may be starting to cough abnormally (indicating respiratory issues), enabling faster intervention. Similarly, vocal responses during stressful events are studied, like, dairy cows isolated from their herd generate characteristic distress calls. An AI model trained on such vocalizations was able to recognize these stress induced calls with nearly 87–89% accuracy, and even identify which cow was calling in many cases (Gavojdian et al., 2024). This capability to pinpoint an individual animal in distress is valuable for large herd management, thus the system essentially gives a specific cow the ability to “call for help” and be heard in a crowd.

Beyond acoustics, PLF systems get multiple benefits from multimodal data integration. Wearable accelerometers and other sensors in addition to audio, help by capturing behaviors like lying, walking, or standing, which may not always be inferred from sound. Combining these streams provides a richer context. For example, pairing vocalizations with activity data can differentiate between a true alarm versus harmless excitement (Russel and Selvaraj, 2024). Collar and leg motion sensors achieved high accuracy in classifying cow activities (like resting vs. feeding) and further used those patterns to detect early signs of illness (mastitis) with over 85% accuracy (Shi et al., 2024). This illustrates how AI can synthesize signals to not only monitor routine behavior but also flag subtle health issues before they become severe. Different ongoing research is addressing issues like computational resources, noisy farms, data limitations through better noise filtering, energy efficient algorithms, and training on more diverse datasets to generalize across environments.

These on-animal IoT systems effectively turn each cow into an active sensor node. By continuously translating jaw movements and vocal sounds into data, they shift farmers from reactive crisis-responders to proactive caregivers. Trusted alerts about subtle welfare signals (like reduced chewing or a distress call) can improve outcomes, but only if the farmer believes “the cow alarm means something’s wrong.”

Several pilot projects and products illustrate how vocalization AI is being deployed. For instance, independent of collars, some farms install fixed microphones in barns or pens to monitor group-level sounds. Study (from a pig-farm study) shows ceiling mics capturing grunts and coughs for automated analysis (Vranken et al., 2023). In cattle, similar acoustic arrays have been trialed, one trial used a network of barn microphones to detect calf distress calls amidst background noise. In another case, a hybrid CNN-LSTM system processed calf vocalizations in real time to alert caretakers when calves were calling frequently (a sign of separation distress). Likewise, startups have begun integrating voice interfaces: a prototype voice assistant, for example, could announce “Cow 17 is in heat” based on detected estrus moos. Across these pilots, performance is mixed. Field evaluation of a CNN-based cow-call detector by Vidana-Vila et al. (2023) (trained on two farms) achieved only ∼57% F1-score (about 74% recall) when evaluated cross-site, indicating many missed calls or false alarms in new settings. In practice, farmers report that such systems can improve vigilance. For example, early coughing alerts allowed earlier treatment of respiratory illness, but also warn of false alarms. Usability factors also matter, battery life, connectivity, and ease of interpreting alerts all influence adoption. Several farmers said they would only trust a “vocalization alarm” if false alerts were low and the interface was simple.

Real-world tests show promise but also caution. Smart collars and barn mic systems can indeed catch important events (estrus cycles, cough outbreaks) early. However, their welfare impact hinges on reliability and usability. High false-alert rates or complex dashboards could negate benefits. Designers must balance sensitivity with precision and involve farmers in tuning systems for their herd and workflow.

A major benefit of decoding cow vocalizations is the potential for early detecting signs of trouble in a cow’s vocal signals before the situation worsens. Cattle often give subtle signals of distress or need through changes in their call patterns. By continuously monitoring these vocalizations, systems can serve as watch-keeper who will alert farmers to issues that might be missed. An unusual coughing sound can flag a respiratory infection, a distinctive mating call can alert that a cow is in estrus, and an increase in urgent, prolonged mooing may signal pain or distress (Gavojdian et al., 2024; Sattar, 2022). Bioacoustic research has shown that we can sometimes reduce the stress by using sound as a gentle intervention. An example is the use of calming cattle vocalizations, like playing a recorded mother-cow call (a low soothing “moo” used by cows to calm their calves) significantly reduced stress in other cows during a stressful restraint procedure (Lenner et al., 2023). Similarly, gentle human vocalizations have been shown to induce positive relaxation in cattle heifers that heard a soft, soothing voice (either live or via recording) while being stroked exhibited signs of comfort, with live speaking having a slightly greater effect (Lange et al., 2020). These findings suggest that farmers can not only listen for distress, but also “talk back” to their animals in a sense using known comforting sounds to ease cattle during events like veterinary exams, transport, or isolation. Such interventions, informed by an understanding of cow communication, can prevent a minor stress from increasing into a major welfare problem. When the system flags an unusual bellow, we still double-check it against the baseline in Table 1 before sounding the alarm.

Implementing vocalization-based alerts on the farm does require careful coordination. Clear, spoken feedback like the example in Figure 6 can also ease ‘alert fatigue’, giving farmers a reason to trust each ping. The system should distinguish meaningful distress signals from the normal noise of a herd. Cows vocalize for positive reasons too (for example, a hungry cow may bellow when she anticipates feeding time), so context is everything. Advanced AI models address this by incorporating contextual information and multiple features so that, for instance, a high-pitched call accompanied by restless movement might trigger an alarm, whereas similar sounds during feeding might be logged as normal (Sattar, 2022). Moreover, not all suffering animals vocalize, but instead some cows may suffer in silence due to their personality or social status. Therefore, the best early warning systems combine multiple data streams like pairing vocal calls with behavioral and physiological data to catch problems that a microphone alone might miss (Neethirajan, 2022). For example, a cow that stops ruminating and remains motionless might be ill even if she isn’t making any sound. Looking forward, researchers wanted creating digital animal profiles that continuously assimilate vocalization data with other biometrics to predict health and welfare status (Neethirajan, 2022). In essence, the cow’s voice becomes one channel in a holistic monitoring network.

By enabling more proactive care, vocalization analysis can considerably improve welfare on farms. Instead of waiting to react after a cow is visibly sick or extremely agitated, farmers get the chance to intervene at the first sign of discomfort. This not only reduces animal suffering but can also enhance productivity, since animals under less stress tend to maintain better appetite, immune function, and milk letdown. Early treatment of issues like illness or distress often leads to faster recovery and lower costs. The key to success will be ensuring these AI tools are user-friendly and reliable in real farm conditions (Sattar, 2022). In sum, giving the herd a continuous voice through AI allows the farm team to become more like attentive caregivers than crisis responders, catching welfare issues early and addressing them before they escalate.

Early trials of AI-based cattle vocalization monitoring indicate tangible economic benefits on farms. By alerting farmers to health issues sooner, these systems enable earlier interventions that reduce treatment costs and production losses. For example, an AI sensor platform detected mastitis signs 1–2 days earlier than usual, allowing treatment to start sooner and preventing severe cases (Liu et al., 2020). This timeliness is critical, since mastitis can otherwise cost roughly €240 per cow annually in treatment and lost milk (about 336 kg per case (Liu et al., 2020). In practice, farmers using an intelligent monitoring assistant (“Ida”) reported significantly shorter cow recovery times and fewer days on antibiotics, thanks to early illness detection (Liu et al., 2020). Reducing clinical outbreaks not only lowers drug expenses but also avoids milk yield drops and cull losses. Similarly, automated acoustic surveillance of calf barns has shown that cough-based alerts often precede visible respiratory disease symptoms (Vandermeulen et al., 2016). By treating calves at the first sound of trouble, producers can curb mortality and limit medication courses. Economic analysis by Vandermeulen et al. (2016) notes that although adding such sensors has upfront costs, early detection saves money via lower mortality, fewer treatments, and improved weight gains in recovered animals. In swine operations, a comparable always on audio AI (SoundTalks^®) enabled interventions up to 5 days earlier than standard checks (Eddicks et al., 2024). Pigs receiving the earliest AI-triggered care showed higher average daily gain and required fewer individual drug treatments (Eddicks et al., 2024). These early results suggest that proactive vocalization monitoring can yield a strong return on investment through healthier, more productive livestock and reduced labor and veterinary expenditures.

Introducing AI-based vocalization monitoring into cattle farming is not only a technical innovation but also ethical one. These systems, by capturing the voice of the animals, reinforce the responsibility of humans to respond. From a welfare science perspective, a comprehensive assessment of dairy cow well-being should include physical health, behavior, and emotional state (Linstädt et al., 2024). An automated system that detects vocal expressions of stress or satisfaction effectively adds a new dimension to such assessments that reflects the animal’s internal state in real time. A traditional welfare audits often rely on periodic checks for injuries, lameness, or abnormal behavior, which can miss short pain or fear episodes (Linstädt et al., 2024). Continuous acoustic monitoring can fill this gap, i.e. if a cow is frequently vocalizing in a distressed manner, that information can prompt caregivers to investigate and address underlying issues sooner. In this way, AI augments human observation and ensures that an animal’s subjective experience (indicated through her calls) is not overlooked.

Importantly, improving how we monitor cows aligns with both ethical principles and practical interests. Animals that are well cared for free of pain, fear, and chronic stress tend to be more productive and healthier, a point not lost on industry stakeholders. Consumers increasingly demand that farm animals be treated humanely, and farms adopting technologies to actively safeguard welfare can bolster public trust. However, with greater insight into animal well-being comes an ethical mandate to act on it. It would be unacceptable to use a system that flags distressing conditions yet not weaken those conditions. Research already shows that certain routine practices can cause significant stress vocalizations (e.g. abrupt separation of calves from mothers leads to increased calling by both (Johnsen et al., 2021). If AI monitors consistently indicate that a particular procedure is distressing the animals, ethical farming practice would force managers to modify that procedure or find alternatives to reduce suffering. In essence, by giving animals a clearer “voice”, technology demands that we listen and respond compassionately.

The advanced tools could be employed to genuinely enhance welfare, thus catching discomfort early and enabling gentler handling strategies. Or they could be misused as basic tools to boost production without regard for the animal’s perspective. In simple terms, if cows are vocalizing distress frequently, the solution is not only to quiet them with calming sounds or algorithms, but to ask why they are distressed and remedy those root causes. The vision of “decoding cow language” is powerful, it suggests a future where farm animals can effectively communicate their needs and feelings to us. Employing these technologies in humane ways can directly benefit the animals; study by Lenner et al. (2023) showed that playing a calming maternal call to stressed cattle significantly reduced signs of fear, indicating how insight from bioacoustics can be translated into kinder handling practices.

While AI listening devices can elevate care standards by catching hidden distress, they also impose a responsibility: to act on the information. The promise is a farm where every vocal plea is heard, leading to timely help. But farmers must trust the tools and avoid complacency. In practice, this means balancing technological alerts with good husbandry using AI as a guide, not a substitute for empathy.

Even the most advanced barn AI systems can mis-interpret a tractor’s rumble as a distress call, revealing potential gaps in data quality, contextual understanding, and ethical considerations. These issues must be critically addressed through rigorous validation, transparent system design, and responsible deployment practices before such tools are implemented at scale across farms.

A major bottleneck is simply data: high-quality, annotated cow audio corpora are scarce. Review by Martinez-Rau et al. (2023 a) found only two public datasets for cattle sounds, one with just 52 grazing/rumination recordings and another with ∼270 annotated calls. By comparison, human speech and wildlife audio benchmarks are orders of magnitude larger. This scarcity makes it hard to train and test robust models. Many studies have been constrained by limited data, which hinders model training and generalization. Model’s reliability is hampered by a small, homogeneous audio sample (Avanzato et al., 2023). Therefore, underscoring the risk of overfitting and poor performance in real farm conditions. Several approaches are emerging to reduce data scarcity. One is the creation of open, shared datasets. However, the open datasets must be sufficiently diverse (capturing various farm environments and herd conditions) to be broadly useful, a point emphasized by Martinez-Rau et al. (2023 a). The steady expansion of datasets we highlighted back in Figure 1 reminds us why new collection efforts must now focus on cross-farm diversity, not just size.

Another strategy is data augmentation, generating synthetic variations of recordings to expand the training pool. Data augmentation and synthesis (e.g. audio GANs) are still underexplored in bovine bioacoustics. Promisingly, self-supervised techniques can mitigate labels, for example, the AVES framework pretrained a transformer on vast unlabeled animal audio, then fine-tuned on tiny, labeled sets (Hagiwara, 2023). Augmenting sensor data can significantly improve a classifier when real samples are limited, and similar augmentation techniques (adding noise, time-shifting, etc.) could support acoustic model robustness (Li et al., 2022). Similar efforts in cattle behavior sensing show that augmenting datasets yields more generalized models (Li et al., 2021). Few-shot learning has also been explored to cope with extremely sparse data (e.g., training a detector with only five example sounds in Paper (Nolasco et al., 2023). In our field, leveraging such self-supervision or transfer learning could substantially reduce labeling needs.

In addition to producing more data, researchers are improving how data is labeled and utilized. Moreover, annotating cow vocalizations is labor-intensive: experts must watch videos or manually label sounds frame by frame. This annotation burden means most studies rely on small, homogeneous samples, raising overfitting concerns. Study by Pandeya et al. (2020) proposes using a trained sound event detection model as a semi-automatic annotator to rapidly scan long recordings and mark potential call events, which experts can then verify. Also, we have active learning where the model iteratively asks for human labels on uncertain cases. This is another promising but underexplored technique to maximize the yield of limited expert labeling time (Stowell, 2022).

Thus, expanding the data foundation is critical. Large, shared datasets (diverse farms, breeds, environments) and creative labeling approaches (e.g. active learning, crowdsourced tagging) would break current limits. In the meantime, tools like AVES suggest we should also design benchmarks and models that assume few labels. Building on models trained on all species or on massive human speech data may be the way forward.

Cow vocalizations vary widely by context: a Holstein in a calm barn calls differently than a Brahman on a breezy range. Breed “dialects”, farm routines, microphone setups, season and even weather can shift acoustic signatures. This variability has emerged as a major challenge, where models trained on vocal data from one scenario or subset of animals may not perform well in another. For example, individual cows’ behavioral and vocal responses to calf separation varied dramatically (Vogt et al., 2025). Some cows vocalized intensely under stress while others remained comparatively quiet. This finding suggests that AI models should look for such individual differences, perhaps by incorporating each cow’s characteristics or baseline vocalization profile into the analysis (Hasenpusch et al., 2024).

As discussed earlier, the contextual meaning of vocal signals is equally crucial. A high-pitched call might indicate acute distress during sudden isolation, yet a similar-sounding call could be routine during feeding or milking. Without contextual information, an algorithm could misclassify the latter as an alarm. Few studies have systematically tackled this variability. As a result, a classifier tuned on one herd often performs poorly on another. Cross-farm tests frequently show performance drops (e.g. an estrus detector trained in one herd missed many heats when moved) (Vidana-Vila et al., 2023). This gap highlights a need to incorporate diverse contexts during training (data from multiple farms, seasons, feed regimes). Future benchmarks and models must explicitly account for inter-breed and environmental heterogeneity.

One strategy is to integrate additional data streams or metadata that capture the circumstances of each vocalization. Pairing vocal analysis with physiological indicators (heart rate, cortisol levels) can be used to infer the emotional state behind a call (Gavojdian et al., 2023). If a cow’s call coincides with an elevated heart rate, it is likely stress-related, rather, a call occurring while the cow’s vitals are normal might be painful communication. To address variability, it will require richer datasets and adaptive models. Datasets should contain diverse contexts and include annotations about the environment or management events associated with each call. Likewise, models may need to explicitly incorporate context (as an input feature or modular component) and adapt to individual differences. An algorithm might learn each cow’s typical vocalization patterns when calm, and flag deviations from that personal baseline as potential distress.

The “one model fits all cows” assumption doesn’t hold. Farmers know that what’s normal in one herd may be crisis in another. Addressing this requires both broad data sampling and robust modeling, For example, training on multi-farm datasets and testing models in truly independent herds. Doing so will increase trust that a “cow vocalization translator” works on your farm, not just the lab.

Current AI models often overfit the idiosyncrasies of their training data. With small datasets and complex networks, models may inadvertently learn farm-specific echoes or even individual voices. This hurts reproducibility: an algorithm showing 90% accuracy in one paper may fail when another team tries it on their herd. Indeed, very few published studies share code or standardize testing, making progress hard to compare. To improve robustness, future work must emphasize cross-validation on independent herds and open benchmarking. Another understudied gap is sensor calibration. Microphones and audio interfaces differ in frequency response and sensitivity. Without calibration or normalization, a “mooooo” recorded on one device may appear as a different signature on another. Building AI that generalizes thus requires methods to calibrate or learn device invariant features. Techniques like adding calibration tones, using consistent sampling standards, or learning calibration transforms should be explored. In sum, the field needs reproducible, open practices and attention to hardware variation to move from lab results to farm-ready tools.

As we incorporate additional data streams in addition to audio, a new challenge arises, harmonizing these multimodal inputs so they truly complement each other. In principle, combining vocal cues with visual observations should yield a more complete picture. For example, a cow’s call paired with her body posture can signal pain versus hunger more clearly than either alone. Yet in practice such contrasting data are difficult to integrate, and current AI models struggle to jointly analyze them. Generally, the vision language models often mislabel or even hallucinate descriptions of cow activities, and they struggle with temporal sequencing of behaviors (Wu et al., 2024 a, b). Complex farm scenes with multiple overlapping animals and variable lighting will further confuse these models. Thereby, indicating that general-purpose AI requires substantial adaptation or fine-tuning for livestock applications. (Wu et al., 2024 a, b).

To address these issues, we must develop better multimodal fusion techniques and training resources. Audio and video streams need to be aligned in time (so the system knows which animal’s vocalization pairs with which visual event) and integrated in a way that each modality’s strengths compensate for the other’s weaknesses. It is worth to note that it is equally crucial to create a combined audio-visual dataset of cattle behavior (Bendel and Zbinden, 2024). This will enable models to learn cross-modal associations from real examples.

Overcoming these challenges will require a cultural shift, sharing code and data (when possible) and rigorously testing in new environments. It also means designing algorithms with real-world messiness in mind for example, by training models to ignore device-specific artifacts or by including calibration as a step. Only then can results be trusted and replicated across studies.

A long-term solution is a standardized benchmark suite tailored to cattle acoustics. Such a suite would define common tasks (e.g. call-type classification, distress detection, individual identification, grazing vs. rumination segmentation) and provide public datasets and metrics. The BEANS (Benchmark of Animal Sounds) project is an inspiring template: it aggregates 12 datasets across species and tasks (classification and detection) to allow apples-to-apples comparison (Hagiwara et al., 2023). Similarly, a “CattleBEATS” benchmark might include audio from multiple breeds and farms, annotated for key events. It would offer baseline results (as BEANS did) and encourage researchers to submit new models. Importantly, the benchmark should incorporate lessons from modern frameworks: for example, it could provide a large unlabeled corpus and challenge participants to use self-supervised learning as in AVES (Hagiwara, 2023), then fine-tune on small labels. It could also encourage evaluation of on-device models: metrics like inference time and energy (inspired by EdgeNeXt) should be reported. EdgeNeXt itself is a compact CNN-transformer that achieved strong image classification and detection accuracy with very low compute (Peng et al., 2024). Adapting an EdgeNeXt-like architecture as a baseline in our field would push efficiency; thus, the benchmark could compare both raw accuracy and edge efficiency. Finally, the suite must promote transparency: all data and code should be open (BEANS and AVES code are public) (Hagiwara et al., 2023; Hagiwara, 2023), and challenge rules should require a clear train/test split across farms. Together, these elements would establish a common framework for progress.

Again, as discussed earlier, another critical gap is the limited generalization of current models beyond the specific conditions they were developed in. To achieve broad applicability, we must embrace data diversity and model adaptability. Deliberately sampling various herd sizes, feeding systems, climates, and so on will teach models to handle variability. When truly representative data are hard to gather, domain specific augmentation techniques can introduce some variability (Li et al., 2022). While, on the model side, algorithms should be designed and tested with generalization in mind. This means using validation methods that reflect real deployments. For example, testing on separate herds rather than random splits within one herd (Riaboff et al., 2022). Models might also include adaptive components to recalibrate to new conditions. Such as establishing each cow’s individual baseline to detect anomalies (this approach used in lameness detection) (Volkmann et al., 2021). Rigorous field trials will be important to refine generalization. Deploying prototype systems on multiple farms and tracking their performance over time can indicate failure modes. For example, a model confusing a new machinery noise for a cow call which can then be addressed. The dataset shortfalls become glaring when you look at the sample-hungry models in Table 5; a common benchmark would let us compare them fairly.

Creating a shared benchmark would galvanize the field. By defining standard datasets, tasks and metrics and by building on BEANS, AVES and EdgeNeXt the community can ensure every new technique is tested fairly. This “nutrition label” for cow audio AI would accelerate progress and foster trust. Ultimately, a well-designed cattle vocalization benchmark can transform isolated prototypes into reliable tools that farmers everywhere can adopt. Table 6 pairs each big research gap like data scarcity or bias with a concrete fix the community is now testing.

The most accurate AI system will also have limited impact on animal welfare if end users do not trust or understand it. Explainability and user confidence are thus pivotal. Farmers, veterinarians, and other stakeholders need to know not just “what” the model predicts, but “why”, especially when algorithms flag something as sensitive as animal distress. To bridge this gap, we should pursue several strategies for explainable and trustworthy AI as:

• Bias reduction and transparency: Proactively identify and correct biases so that the model performs reliably across different herds and conditions (Stowell, 2022). This includes diversifying training data and auditing model outputs for systematic errors, ensuring no group of scenarios is overlooked.

• Output calibration: Calibrate the AI’s confidence scores so that probability outputs align with actual accuracy (Stowell, 2022). Users can then interpret a “90%” distress prediction as truly high likelihood, and the system can signal low-confidence detections to invite human review rather than acting on a guess.

• User friendly interfaces with explanations: Design intuitive dashboards that present alerts in context. For example, the interface might display a timeline of detected calls and highlight the acoustic features that led to a “distress” classification, along with a brief explanation. (Looking ahead, Figure 8 sketches a future hybrid model that links incremental sensor upgrades to long-term welfare goals.) By visualizing what the model “heard”, such tools let users verify the AI’s reasoning (Stowell, 2022) and learn to trust its alerts.

• Preventing over-reliance: Clearly communicate the AI’s supportive role to users so they treat it as an aid, not a decision-maker authority (Bendel and Zbinden, 2024). Farmers should be encouraged to continue observing their animals. The AI is an assistant that might catch subtle cues, but human judgment remains crucial for context.

• Field validation and iteration: Rigorously test the system in real farm conditions and iteratively improve it based on those results (Prestegaard-Wilson and Vitale, 2024).

Figure 8.

Hybrid explainable AI multimodal (HEAM) pipeline. Four synchronous streams audio, video, collar IMU signals and environmental sensors are preprocessed and fused into a unified 320-dimensional feature vector. A gradient-boosted decision-tree classifier provides transparent rule-based predictions, while a large language model (LLM) converts those rules plus sensor context into plain language guidance for the farmer. A feedback loop allows new labeled events to fine-tune the CNN front-end and refresh the tree, enabling continuous on-farm adaptation without sacrificing interpretability

Table 7 summarizes the principal technical and ethical hurdles identified in this review and pairs each with actionable solution pathways. By prioritizing interpretability, fairness, and collaboration in design, future bovine bioacoustic AI tools can earn the trust of their human users. Such systems will not only be technically sound but also practically effective, as farmers feel confident integrating them into daily welfare management.

Designing a barn from the ground up where every vocalization, movement, and heartbeat contributes to an integrated smart system mapping each cow’s daily life – requires careful selection of foundational components, including reliable sensor technologies, robust machine learning algorithms, and continuous incorporation of farmer feedback, to ensure the system is both practically viable and aligned with animal welfare. Building on the gaps identified in previous sections, we outline a forward-looking framework to advance AI-driven bovine bioacoustics. This framework is organized into five key components: a conceptual model for AI-augmented cow communication, data acquisition and annotation strategies, ethical fusion in deployment, adaptive and explainable model development, and hybrid explainable models. Collectively, these elements chart a research roadmap toward practical systems that “listen” to cows and translate vocal cues into actionable welfare insights.

We envision a modular Smart Farm system that unites audio bioacoustic sensors with other livestock sensors in a tiered architecture (Figure 7). At the animal level, wearable collar devices incorporate inertial measurement units (accelerometers/gyroscopes), GPS, and microphones to capture each cow’s movement, posture and vocalizations (Lamanna et al., 2025; El Moutaouakil and Falih, 2023). In the barn, distributed sensors include directional or ambient microphones, thermal/infrared cameras, and environmental probes (temperature, humidity, air quality) to monitor herd-level cues (El Moutaouakil and Falih, 2023; McManus et al., 2022; Eckhardt et al., 2024; Holinger et al., 2024). These sensor nodes stream raw signals to local edge processors. We propose using lightweight CNNs (for example, TensorFlow Lite’s MobileNet or next-generation EdgeNeXt models) on low-power hardware to perform on-board inference (e.g. call detection, gait analysis) (Noda et al., 2024; El Moutaouakil and Falih, 2023). Only compact, coded results (e.g. detected events or summary statistics) are then relayed to a higher level AI fusion engine. The fusion layer integrates the multimodal inputs audio features, motion patterns, and heat signatures into a unified welfare score or alert via deep learning or probabilistic models. Finally, actionable insights are delivered to farmers through cloud dashboards and mobile apps, closing the loop with real-time feedback and visualization. A data fusion interface will align timestamps and meta-data (e.g. cow ID from ear tags) so that, for example, a detected distress call can be correlated with a cow’s recent activity or elevated body temperature.

Collar sensors: Wearable units with 3-axis accelerometers, gyros, GPS/RFID, and optionally microphones or temperature sensors (Lamanna et al., 2025; El Moutaouakil and Falih, 2023).

• Barn monitors: Fixed microphone arrays for group vocalizations, infrared cameras for thermal imaging, environmental sensors (temp/humidity) and video feeds.

• Edge AI modules: Embedded microcontrollers running efficient CNNs (e.g. MobileNet) for on-device detection of calls or behavior (Noda et al., 2024).

• Fusion interfaces: Middleware that time aligns audio, motion and thermal data streams for joint analysis.

• Cloud/Server analytics: Powerful AI models (possibly including transformer-based audio classifiers) that integrate fused data across animals and time.

• Farmer dashboard: Web/mobile apps presenting alerts and summaries. These interfaces should support user feedback or annotation and allow parameter tuning (e.g. alert thresholds) by the farmer (El Moutaouakil and Falih, 2023; Noda et al., 2024).

This real-time pipeline thus continuously collects sensor data, processes it locally (to reduce bandwidth), and feeds the key results into a central inference engine. All system outputs from individual call classifications to herd-level stress indices are logged and visualized for the farmer. Importantly, the design allows multi-modal correlation: e.g. a series of high-pitched moos aligned with a cow’s rapid movements and a spike in thermal readings would trigger a high welfare alert score. Modular interfaces mean new sensor types or AI models (e.g. future EdgeNeXt modules) can be plugged in without redesigning the entire system.

Data is the fuel of any AI system. As we have seen, a robust strategy for data acquisition and annotation is the foundation of our framework. Current literature review highlights that obtaining large, representative, and accurately labeled datasets of cattle vocalizations is a major bottleneck. Many studies to date have relied on relatively small samples of audio recordings with labor intensive manual labeling, thereby limiting model generalization and insight depth. Automating labels should also shrink the right-hand ‘data excluded’ box in Figure 2 during our next literature sweep.

One promising approach is to deploy continuous audio recording on farms using affordable IoT microphones. These microphones are coupled with automatic vocalization detection algorithms. Initial steps in this direction show that convolutional networks can detect cow calls within noisy farm audio streams, reducing the burden of manual screening. For instance, an automatic cow call detector that scans continuous audio and flags segments containing vocalizations (Li et al., 2024). It achieves high recall with minimal false positives. Integrating such detectors on-farm means that vast amounts of sound data can be collected and pre-processed with minimal human intervention. This system itself marks potential calls for further analysis. This forms a feedback loop for annotation, instead of an expert combing through audio blindly, the AI provides a first pass, and human annotators then verify and refine the labels for those flagged segments. Including data from multiple farms during training improves the detector’s robustness, suggesting that broad data collection (different herds, breeds, and environments) is key to building generalized models (Li et al., 2024).

Beyond detection, labeling the meaning or context of each call is another challenge. Research should explore AI-assisted labeling where preliminary classifiers or clustering algorithms assign tentative tags (e.g., “distress call” vs. “normal call”) that the experts can confirm. Active learning is a valuable standard here. The model can highlight uncertain cases, like a vocalization it finds confusing and demands an expert labeling, thereby efficiently focusing human effort where it is most needed. Over time, this strategy could dramatically cut down the human labor per additional hour of recording. Indeed, using machine suggestions to guide human annotators has proven effective in other bioacoustic domains (for example on bird call annotation) (Geng et al., 2024), and it is a natural fit for bovine vocal data where expert labeled examples are sparse.

To facilitate this, it will be important to develop a standardized annotation schema for cow vocalizations. This schema would define the set of call categories or states (e.g., isolation distress, maternal call, pain moan, feeding call, etc.) and include metadata like environmental context and concurrent animal behaviors. By consistently recording metadata (such as weather, time of day, herd activity during the call, position and video of the cattle), we can later utilize this information in modeling. We envision tools like mobile or web apps that farmers or researchers in the field can use to quickly annotate notable vocal events with just a few taps. For instance, tagging a spontaneous loud moo with “calf separated”. Over time, such crowd sourced labeling by trained farm personnel could contribute to large, annotated corpora. Of course, quality control is vital, thus, part of the strategy is to incorporate validation steps, where a subset of the crowd labels is cross-checked by experts or by agreement across multiple annotators (ensuring annotation quality in large-scale animal sound projects) (Sun et al., 2023). This strategy directly addresses the data scarcity and annotation burden identified in current research. It lays the groundwork for more accurate and context aware vocalization decoding systems.

Introducing AI monitoring into farms raises ethical considerations. First, black-box decisions are a concern. If an AI alerts “Cow #57 distressed” without explanation, farmers may distrust or over-trust it. We must guard against opaque welfare judgments. Combining explainable models with human review is essential. Second, alert over-reliance and false positives can erode trust and animal well-being. Erroneous alarms (e.g. false distress calls) can lead farmers to take unnecessary actions, while missed alerts risk unseen suffering (Tuyttens et al., 2022). Systems should be calibrated for low false-alarm rates and framed as decision-support rather than decisions. Third, surveillance concerns arise, continuous audio/video recording can feel intrusive, exposing farmers to scrutiny (Tuyttens et al., 2022). It is critical to limit data use strictly to welfare assessment and to secure consent, reminding users that data should empower (not penalize) them. Finally, we must resist technological determinism, technology should not dictate farming culture. Farmers’ expertise and compassion remain central. The aim is a partnership AI as an “extra pair of ears”, not a replacement for human care.

AI-powered livestock monitoring promises benefits but must be balanced with ethical safeguards. Transparency and farmer agency are paramount to prevent blind trust in algorithms. Careful design (explainable alerts, error thresholds) and policies limiting surveillance misuse will help align smart-farm technologies with humane farming values.

To fully realize the benefits of AI in bovine bioacoustics, we must address two critical needs: adaptability and explainability.

• Adaptive AI systems: Cattle vocalization models will encounter varying acoustic environments, herd, and even individual originality. A model trained in one scenario can drop in performance when deployed to another if it cannot adapt. Indeed, cross-study evaluations have shown notable drops when applying a classifier to unseen conditions. As seen earlier, a vocalization detector trained solely on Farm A’s data saw its accuracy dip when tested on Farm B, having different noise profile and herd composition (Li et al., 2024). These findings highlight both the challenge and a solution. The models need exposure to diverse data and mechanisms to adapt to new inputs. One approach could be to have a small set of calibration recordings for any new deployment. For example, record a few hours of ambient sounds and some typical calls when installing the system on a farm, then fine-tune the model on that. Another approach is transfer learning, where a base model trained on a large corpus of cow calls can be lightly retrained on a specific herd’s data to personalize it. Even a few dozen labeled samples from the target farm are enough to significantly boost performance to near native levels (Nolasco et al., 2023). Beyond this, future AI systems might employ online learning. They should continuously update their models as new data comes in.

• Explainable AI (XAI): While powerful, these adaptive deep learning models will not give explanations. Farmers are more likely to trust and adopt AI if they can understand the basis of its alerts or recommendations. Imagine an AI alert that simply says, “Cow #108 is distressed.” The farmer’s natural response is to ask why the AI concluded that. Whether, it was a certain type of moo, a pattern of vocalizations over time, or a combination of vocal and movement signals? If the system cannot provide a clear rationale, the farmer may be skeptical or unsure how to act on the alert. On one hand, interpretable models might use human comprehensible features (e.g., call rate, pitch range, duration) in simpler algorithms to output decisions that align with expert knowledge. For example, “high-pitched repeated calls + restless movement → likely separation anxiety”. In fact, one of the approaches was building an explainable model that used defined acoustic features and AutoML to produce rules for classifying calls, allowing the contribution of each feature to be assessed. The “white-box” model could distinguish high vs. low frequency calls with around 90% accuracy, and importantly, it could highlight which features were contributing each classification. The downside was that a more complex deep learning model slightly outperformed the explainable model on accuracy. It is a common trade-off in AI.

To maximize accuracy, context-awareness, and farmer trust, we extend the Hybrid Explainable Acoustic Model (HEAM) into a Multimodal LLM HEAM:

• Acoustic front-end – A lightweight CNN (e.g., MobileNet-Spectro; Vidana-Vila et al., 2023) transforms each 1-s Mel-spectrogram into a 256D embedding that captures subtle spectral patterns of individual call types.

• Complementary sensor streams –

  • Video: A compact vision model (e.g., YOLO-Nano; Peng et al., 2024) outputs per cow posture (standing, lying, pacing), rumination, jaw motion, and proximity to conspecifics.

  • Accelerometers: Collar IMUs provide step count, activity intensity, and rumination bouts, following the chew monitoring (Martinez-Rau et al., 2023 b).

  • Environment: Barn sensors log temperature–humidity index (THI), noise level, and light, variables shown to modulate vocal stress responses (Martinez-Rau et al., 2025).

• Feature bridge – For each 15-s window, the acoustic embedding is concatenated with handcrafted audio features (median F0, call rate) and low-dimensional descriptors from video/IMU/THI streams, yielding a unified vector that retains human-intuitive cues (pitch, activity) while injecting multimodal context (Peng et al., 2024).

• Surrogate decision-tree layer – A shallow gradient-boosted decision tree (GBDT) trained on the unified vector yields if-then rules (e.g., high-pitch call + pacing + THI > 72 → heat-stress risk). The tree enforces monotonic splits that follow domain logic.

• LLM reasoning agent – The rule output and sensor summary are passed to a lightweight LLM (prompt-orchestration concept adapted from AudioGPT) (Huang et al., 2024). The LLM rewrites the rule in plain language, adds context (“pacing + THI 78”), and suggests actions (e.g., activate fans).

• Explanation and feedback – The LLM returns a concise rationale:

  “Alert: Cow 108 likely heat stressed (confidence = 92%).

  Why: pitch = 640 Hz (>550 Hz); calls = 8/min; accelerometer shows pacing; THI = 78.

  Recommendation: Activate fans or move to shaded pen; recheck in 15 min.”

• Farmer UI – A dashboard (mobile/web) displays the alert, underlying rule, and LLM recommendation. Farmers can accept, snooze, or label the alert, generating feedback for online adaptation: the CNN fine-tunes on new audio, and the tree/LLM prompt templates update incrementally, sustaining accuracy across changing farm conditions.

HEAM is proposed to fuse deep learning features with transparent decision rules and rationales. In HEAM, a CNN first converts an input cow vocalization (e.g. a spectrogram) into a compact audio embedding vector capturing salient acoustic features. This embedding is then fed into a lightweight decision tree classifier that outputs a predicted call category and a human-interpretable decision path (e.g. “if feature_5 > 0.8 and feature_2 < 0.1 then distress call”). Unlike pure “black-box” networks, the tree provides explicit if-then rules that can be inspected for each prediction. An LLM component subsequently generates a natural language rationale by summarizing the decision tree’s logic and the audio features of the example (Pei et al., 2025). This hybrid approach yields both high-level accuracy and explainability – the CNN supplies robust feature learning, while the decision tree + LLM combo ensures each alert comes with an explanation that farmers can understand (e.g. “the system flagged a distress call due to unusually high pitch and chaotic vocal pattern”). Algorithm 1 illustrates the HEAM inference loop pseudocode. Notably, if the CNN tree model is uncertain, the system can fall back to simple threshold rules (e.g. prolonged loud bellow triggers an alert) rather than output nothing – providing a safety net. This design improves transparency and trust: farmers not only get an alert but also a reason, and the model’s fallback logic means obvious warning signs will not be missed even if the AI’s confidence is low (Gavojdian et al., 2023).

The above pseudocode shows how HEAM processes each new audio clip: the CNN produces emb features, the decision tree yields a prediction and decision path, and if confidence is low a rule-based classifier provides a fallback decision. An LLM then turns the decision path and label into a farmer-friendly explanation before notification. This hybrid explainable workflow is powerful for frontline users. By pairing a deep model with an explainable decision module, HEAM ensures transparency (farmers see why a call was flagged) and accountability in predictions. It also enhances reliability via fallback: even if the complex model is unsure, simple heuristic rules (like a high-decibel prolonged moo indicating distress) can trigger an alert. Such a system can thus earn farmers’ trust, as it behaves intelligently but can always explain itself and defaults to conservative, rule-based alerts when uncertain.

In the next five years, digital twin technologies in barns may evolve to provide continuous insights into the well-being of each cow; however, it is likely that certain nuances of bovine emotion will remain beyond the interpretive capacity of even the most advanced algorithms.

One vision is the development of interactive AI systems that facilitate two-way communication between humans and cattle. Instead of simply decoding cow vocalizations into human understandable terms, future systems could also generate vocal or behavioral responses from cows. We should begin to imagine “conversational cow AI” platforms where a farmer could receive a message like “I am hungry” from a cow and the system could respond by emitting a sound. Such concepts are a logical extension of current advances in decoding animal signals (Dimov et al., 2023). If AI can reliably interpret a calf’s distress call, it is possible that it could also play back a pre-recorded or AI synthesized call to comfort the calf. Early groundwork in applied animal linguistics supports this bidirectional approach. It treats animal calls as a true language to be both interpreted and actively spoken by AI. The emerging field of “animal linguistics” hypothesizes that animal communications have language-like structure. Using this, an AI could eventually construct responses that fit the specie’s social communication patterns (Lenner et al., 2023). Figure 6 already hints at how a future dashboard might let the cow ‘speak’ first and the system reply with a plan of action.

Enabling two-way conversations will require integration of several technologies. First, accurate real-time translation models must be developed to convert complex vocal signals into meaningful alerts. Second, advancements in voice synthesis for non-human sounds will be critical so that any AI response is in a form the cow can understand. Any interactive system should be developed with ethologists to measure cattle responses and refine the “conversation” protocols in line with natural bovine behavior. In sum, a true dialogue between humans and cows remains a long-term vision. Nevertheless, early steps in AI understanding of animal signals and sound playback technologies indicate the exciting opportunities in animal welfare (Dimov et al., 2023; Cetintav et al., 2025).

Cattle are not the only livestock with meaningful vocal repertoires, but also pigs, goats, sheep, and chickens all vocalize to convey emotions and needs. A unified approach would accelerate learning by using insights from each of the species. Some of methods successful in cattle such as using deep learning to classify vocal emotional ability, have also been applied to pig screams (Chelotti et al., 2023). Similarly, vocal indicators of emotion in goats have been mapped to physiological and behavioral states (Gavojdian et al., 2024). It reinforces the idea that mammals share common acoustic signals for stress, contentment, and social contact. It is a next step to ask whether an AI trained on one species could transfer knowledge to another? For example, whether an algorithm trained to detect distress in cow moos, can it be adapted with minimal retraining to detect distress in goat or pig? By examining how cows, pigs, and chickens each express hunger or pain vocally, we can identify universal acoustic signatures of distress or well-being.

The biggest challenge in this vision is the biological differences between species. An AI must be able to interpret that a cow’s communication evolved in a different social and ecological context than a pig’s. Each species has their different biological structure and evolution time. A universal translator will need species specific tuning and an understanding of context. Still, some concepts might not generalize. For example, chickens have no equivalent of a calf’s maternal-separation call. Here knowledge is shared across disciplines to increase welfare in a variety of species, not only cattle. In conclusion, by expanding beyond bovine communication, it is possible to develop AI systems that are both wider in scope and deeper in understanding. Still applying generalizability on species remains a big challenge.

We outline a five-year technical roadmap to advance AI-driven bioacoustics in precision livestock systems:

• Year 1 (2025): Initiate large-scale data collection, prototyping sensor hardware, and establishing initial partnerships with farms. Develop a basic audio recognition model (e.g. CNN for call detection) and a pilot dashboard.

• Year 2 (2026): Enhance model robustness via transfer learning and domain adaptation. Begin creating open benchmark datasets (inspired by BEANS) (Hagiwara et al., 2023) for cattle vocalizations to standardize performance evaluation. Conduct limited field trials to tune algorithms to on-farm conditions.

• Year 3 (2027): Focus on cross-domain generalization and explainability. Integrate multimodal learning (fusing sound with video and accelerometer/thermal inputs) to reduce false alarms. Develop explainable AI interfaces so farmers see “why” an alert was raised. Begin miniaturization of sensing hardware and optimizing power (e.g. sub-1W edge chips).

• Year 4 (2028): Scale to multiple farms and conditions. Publish comprehensive performance benchmarks on unseen herds. Engage with standard-setting bodies to define best practices for animal sound datasets. Refine edge/cloud orchestration for low-latency alerts. Work toward certifying systems for herd health monitoring.

• Year 5 (2029): Achieve widespread adoption. Demonstrate that AI audio systems reduce animal welfare issues (e.g. earlier sickness detection). Further miniaturize and reduce cost of sensors. Ensure models handle real-world variability (weather, new breeds). Launch farmer education programs to interpret AI feedback correctly.

Throughout this timeline, key milestones include improving model robustness (reducing overfitting to one farm’s acoustics), establishing open benchmarks for livestock sounds (Hagiwara et al., 2023), hardware innovation (lighter collars, integrated devices), and validation of cross-farm generalization. By Year 5, we expect a mature platform, farmers using AI agents to “listen” to cows as reliably as they monitor milk sensors or rumen probes.

Deploying an AI system for cattle vocalization monitoring entails several ethical considerations around bias, reliability, and research integrity. Fairness is a key concern: the model should be evaluated for breed or demographic biases in its performance. For example, if a distress-call detector is trained mostly on Holstein Friesian cows, will it work as accurately on Jersey or Brahman cattle? Conducting fairness audits to check for breed bias and adjusting the training data accordingly is important to ensure equal welfare benefits for all animals. In general, AI models must be validated under the full range of conditions they will encounter, otherwise they risk poor external validity, giving unreliable alerts in new contexts (Tuyttens et al., 2022). A lack of such validation can hide “algorithmic bias”, where the system under-serves certain groups (e.g. specific breeds or ages) while appearing objectively accurate overall (Tuyttens et al., 2022). Ensuring diverse, representative training data and transparently reporting performance across subgroups is part of responsible AI development for animal welfare. Over-diagnosis and alert fatigue present another ethical challenge. If the system generates too many false alarms (e.g. flagging normal calls as distress), farmers may face “cry wolf” situations. Not only can this lead to unnecessary interventions (which may stress animals), but it can also cause the farmer to become desensitized or frustrated (Tuyttens et al., 2022). Unreliable alerts – both false positives and misses – can ultimately harm animals if caretakers start ignoring the system. Thus, the AI’s sensitivity must be calibrated to minimize false alerts, and its alerts should ideally be accompanied by confidence or explanation to convey uncertainty. Performing rigorous field testing helps establish an ethical balance between catching true issues and avoiding spurious warnings.

Finally, we adhere to the ARRIVE 2.0 guidelines (Animal Research: Reporting of In Vivo Experiments) in our research and development process (Percie du Sert et al., 2020). ARRIVE 2.0 provides a checklist for robust and transparent reporting of animal experiments, ensuring that our methods and results can be scrutinized and reproduced. By following ARRIVE 2.0, we commit to high standards of animal welfare (e.g. minimizing distress during data collection) and scientific rigor in reporting. This fosters trust in the system: stakeholders can review how the AI was trained and validated, knowing it was developed under stringent welfare and transparency protocols. In summary, addressing bias/fairness, preventing over-diagnosis, and maintaining rigorous reporting practices (ARRIVE 2.0) are all essential to overcoming barriers to trust and ethical deployment of AI in livestock settings.

In conclusion, the reviewed article shows that bovine vocalizations carry valuable information about cattle welfare and behavior. AI/NLP techniques are used to unlock this information. Decades of acoustic research have been made to show that cows produce distinct calls in different context. These calls are often linked with characteristic frequency patterns and durations. The foundation of AI in bioacoustics has made it possible to input vocalization data into modern analytic pipelines. And machine learning algorithms can learn the precise patterns that differentiate, like a calm contact call from an agitated distress call.

Early supervised learning methods validated this approach. Models like decision trees, SVMs, and RF trained on manually created acoustic features have been able to classify cow calls with reasonable accuracy but in controlled settings. These models demonstrated that even relatively simple audio features contain predictive information. However, they also highlighted many limitations. Hand-engineered features may miss important patterns, and classical models struggle when the acoustic context becomes complex or when the system is moved to a new herd environment. Deep learning methods have improved performance. CNN applied to spectrogram images have achieved accuracy in identifying call types without manual feature design. On the other hand, recurrent and transformer architectures have leveraged temporal context to decode call sequences. These AI systems have proved capable of distinguishing multiple call categories and even detecting sentiments. In some pilot studies, AI-driven audio monitoring systems have successfully alerted farm staff to cows in heat or distress.

These technological advancements bring along equally vital considerations. Most existing models have been tested on relatively small datasets or in controlled trials. Their real-world applications yet remain to be fully validated. Differences in breeds, calls of cattle, farm management styles, and recording conditions indicate that an algorithm trained in one setting may not immediately apply to another. Noise on the farm like other livestock, equipment noise, environmental sounds can still confuse classifiers. Moreover, a focus on maximizing accuracy risks neglecting transparency. If an AI signals a cow is ‘in pain,’ farmers need to trust and understand that decision. Explainable models and user-friendly interfaces will be crucial for adoption.

Looking ahead, the review suggests several directions. Expanding and standardizing datasets is a first priority. Public repositories of labeled bovine vocalizations, drawn from diverse herds and contexts, would accelerate progress and ensure fairness. Multimodal integration holds great promise. Combining audio with video, motion, physiological, or environmental sensors could clarify calls that sound similar but occur in different contexts. For example, a drop in eating related vocalizations might only be meaningful if accompanied by observed reductions in feeding behavior on camera or changes in vital signs. Developing adaptive learning systems, perhaps using on-farm feedback loops or incremental learning could help models stay accurate even in real-time condition. To reach this vision, interdisciplinary collaboration is needed with animal scientists, AI researchers, ethologists, and farmers. Engaging farmers in the design process will help ensure the tools meet real needs. Looking ahead, each improvement in understanding cow vocalizations can be profitable in terms of healthier animals, more informed caretakers, and a glimpse into the inner lives of animals whom we often take for granted.