Attention‑Enhanced Convolutional Neural Network for Music Genre Classification

The complex interplay of harmonic, rhythmic, and timbral elements within musical signals poses a challenge for accurate music genre classification. Conventional approaches have limitations in effectively capturing both localized features and long‑range contextual relationships within the audio data. To overcome this, we introduce an attention‑enhanced convolutional neural network (AECNN) architecture that employs convolutional layers to efficiently extract localized features and an attention mechanism to capture long‑range temporal dependencies within the input audio segment. This integrated approach empowers AECNN to comprehensively learn both local features and segment‑level contextual relationships, resulting in enhanced accuracy and robustness in classifying diverse music genres. The performance of the proposed model is evaluated on two datasets, the widely used GTZAN dataset and the novel IMG‑Amrita dataset, encompassing 12 distinct Indian music genres. Experiments conducted with varying audio sample durations (30, 15, 10, 6, and 3 s) demonstrate the impact of sample length on classification accuracy. Notably, AECNN achieves peak accuracies of 96.02% on the GTZAN dataset and 98.94% on the IMG‑Amrita dataset with 3‑s samples, underscoring its ability to capture intricate musical nuances with limited audio data. These findings highlight the significant potential of the AECNN for robust and versatile performance across a wide range of music classification tasks.