References
- Abbası Layegh, M., Haghıpour, S., and Najafı Sarem, Y. (2014). Classification of the radif of mirza abdollah a canonic repertoire of persian music using svm method. Gazi University Journal of Science Part A: Engineering and Innovation, 1(4), 57–66.
- Agostini, G., Longari, M., and Pollastri, E. (2003). Musical instrument timbres classification with spectral features. EURASIP Journal on Advances in Signal Processing, 2003(1), 943279.
- Azar, S. R., Ahmadi, A., Malekzadeh, S., and Samami, M. (2018). Instrument‑independent dastgah recognition of iranian classical music using azarnet. CoRR. abs/1812.07017.
- Baba Ali, B., Gorgan Mohammadi, A., and Faraji Dizaji, A. (2019). Nava: A persian traditional music database for the dastgah and instrument recognition tasks. Advanced Signal Processing, 3(2), 125–134.
- Baba Ali, B. (2024). On the effectiveness of self‑supervised pre‑trained models for persian traditional music information retrieval. SSRN.
- Beigzadeh, B., and Belali Koochesfahani, M. (2016). Classification of iranian traditional musical modes (dastgäh) with artificial neural network. Journal of Theoretical and Applied Vibration and Acoustics, 2(2), 107–118.
- Bhalke, D. G., Rao, C. B. R., and Bormane, D. S. (2016). Automatic musical instrument classification using fractional fourier transform based‑mfcc features and counter propagation neural network. Journal of Intelligent Information Systems, 46(3), 425–446.
- Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E. (2020). A simple framework for contrastive learning of visual representations. CoRR. abs/2002.05709.
- Ebrat, D., Didehvar, F., and Dadgar, M. (2022). Iranian Modal Music (Dastgah) detection using deep neural networks. arXiv preprint arXiv:2203.15335.
- Essid, S., Richard, G., and David, B. (2006). Hierarchical classification of musical instruments on solo recordings. In 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings , IEEE, Vol. 5, pp. V–V.
- Farajzadeh, N., Sadeghzadeh, N., and Hashemzadeh, M. (2023). Pmg‑net: Persian music genre classification using deep neural networks. Entertainment Computing, 44, 100518.
- Farhat, H. (1990).
Intervals and scales in contemporary Persian music . In Cambridge Studies in Ethnomusicology (pp. 7–18). Cambridge University Press. - Ferraro, A., Favory, X., Drossos, K., Kim, Y., and Bogdanov, D. (2021). Enriched music representations with multiple cross‑modal contrastive learning. IEEE Signal Processing Letters, 28, 733–737.
- Gong, X., Zhu, Y., Zhu, H., and Wei, H. (2021). Chmusic: A traditional chinese music dataset for evaluation of instrument recognition. In Proceedings of the 4th International Conference on Big Data Technologies (pp. 184–189).
- Gourisaria, M. K., Agrawal, R., Sahni, M., and Singh, P. K. (2024). Comparative analysis of audio classification with mfcc and stft features using machine learning techniques. Discover Internet of Things, 4(1), 1.
- Guinot, J., Quinton, E., and Fazekas, G. (2024). Semi‑supervised contrastive learning of musical representations. arXiv preprint arXiv:2407.13840.
- Han, Y., Kim, J., and Lee, K. (2016). Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(1), 208–221.
- Herrera, P., Peeters, G., and Dubnov, S. (2010). Automatic classification of musical instrument sounds. Journal of New Music Research, 32(1), 3–21.
- Heydarian, P., and Bainbridge, D. (2019). Dastgàh recognition in iranian music: Different features and optimized parameters. In Proceedings of the 6th International Conference on Digital Libraries for Musicology (DLfM), New York, NY, USA: Association for Computing Machinery, pp. 53–57.
- Humphrey, E. J., Durand, S., and McFee, B. (2018). Openmic‑2018: An open data‑set for multiple instrument recognition. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
- Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D. (2020). Supervised contrastive learning. CoRR. abs/2004.11362.
- Kumar, P., Rawat, P., and Chauhan, S. (2022). Contrastive self‑supervised learning: Review, progress, challenges and future research directions. International Journal of Multimedia Information Retrieval, 11(4), 461–488.
- Lin, D. (2022). Contrastive Feature Learning for Audio Classification. Electrical Engineering and Computer Sciences.
- Madhusudhan, S. T., and Chowdhary, G. (2024). Deepsrgm–sequence classification and ranking in indian classical music with deep learning. arXiv preprint arXiv:2402.10168.
- Mousavi, S. M. H., Prasath, V. B. S., and Mousavi, S. M. H. (2019). Persian classical music instrument recognition (pcmir) using a novel persian music database. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 122–130.
- Mürer, G. (2021). Music of a Thousand Years: A New History of Persian Musical Traditions. By Ann E. Lucas. Music and Letters, 101(3), 576–578.
- Niu, Y. (2024). Automated classification of chinese traditional music genres using multi‑modal knowledge graph convolutional networks. Journal of Electrical Systems, 20(7), 2939–2951.
- Prabavathy, S., Rathikarani, V., and Dhanalakshmi, P. (2020). Classification of musical instruments using svm and knn. International Journal of Innovative Technology and Exploring Engineering, 9(7), 1186–1190.
- Reghunath, L. C., and Rajan, R. (2022). Transformer‑based ensemble method for multiple predominant instruments recognition in polyphonic music. EURASIP Journal on Audio, Speech, and Music Processing, 2022(1), 11.
- Saeed, A., Grangier, D., and Zeghidour, N. (2021). Contrastive learning of general‑purpose audio representations. In ICASSP 2021‑2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 3875–3879.
- Sharma, A. K., Panwar, A., and Chakrabarti, P. (2014). Analytical approach on indian classical raga measures by feature extraction with em and naive bayes. International Journal of Computer Applications, 107(6), 41–46.
- Singh, P., Gupta, A., and Arora, V. (2024). Novel class discovery for open set raga classification. arXiv preprint arXiv:2411.18611.
- Solanki, A., and Pandey, S. (2022). Music instrument recognition using deep convolutional neural networks. International Journal of Information Technology, 14(3), 1659–1668.
- Spijkervet, J., and Burgoyne, J. A. (2021). Contrastive learning of musical representations. CoRR, abs/2103.09410.
- Yang, J., Gao, F., Yun, T., Zhu, T., Zhu, H., Zhou, R., and Wang, Y. (2025). A deep‑learning framework with multi‑feature fusion and attention mechanism for classification of chinese traditional instruments. Electronics, 14(14), 2805.