HamNava: A Dataset for Multi‑Label Instrument Classification
By: Pouya Mohseni, Bagher BabaAli and Hooman Asadi
References
- Agus, T. R., Suied, C., Thorpe, S. J., and Pressnitzer, D. (2012). Fast recognition of musical sounds based on timbre. The Journal of the Acoustical Society of America, 131(5), 4124–4133.
- Akbari, A., and Gabdulhakov, R. (2019). Platform surveillance and resistance in Iran and Russia: The case of Telegram. Surveillance & Society, 17(1–2), 223–231.
- Asadi, H. (2004). Theoretical foundation of Persian classical music: Dastgāh as a multi‑modal cycle. Māhoor Music Quarterly, 22, 43–50.
- BabaAli, B., and Mohseni, P. (2024). On the effectiveness of self‑supervised pre‑trained models for Persian traditional music information retrieval. ssrn. 4968082.
- Baevski, A., Hsu, W.‑N., Xu, Q., Babu, A., Gu, J., and Auli, M. (2022). Data2vec: A general framework for self‑supervised learning in speech, vision and language. In International Conference on Machine Learning, pp. 1298–1312. PMLR.
- Bittner, R. M., Salamon, J., Tierney, M., Mauch, M., Cannam, C., and Bello, J. P. (2014). MedleyDB: A multitrack dataset for annotation‑intensive MIR research. In Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, pp. 155–160. ISMIR.
- Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., . . . Liang, P. (2021). On the opportunities and risks of foundation models. arXiv:2108.07258.
- Bosch, J. J., Janer, J., Fuhrmann, F., and Herrera, P. (2012). A comparison of sound segregation techniques for predominant instrument recognition in musical audio signals. In Proceedings of the 13th International Society for Music Information Retrieval Conference, Porto, Portugal, pp. 559–564. ISMIR.
- Caro Repetto, R. (2018). The Musical Dimension of Chinese Traditional Theatre: An Analysis From Computer Aided Musicology [PhD. thesis]. Universitat Pompeu Fabra.
- Caro Repetto, R., Pretto, N., Chaachoo, A., Bozkurt, B., and Serra, X. (2018). An open corpus for the computational research of Arab‑Andalusian music. In Proceedings of the 5th International Conference on Digital Libraries for Musicology, DLfM ’18, New York, NY, USA, pp. 78–86. Association for Computing Machinery.
- Défossez, A., Copet, J., Synnaeve, G., and Adi, Y. (2022). High fidelity neural audio compression. arXiv:2210.13438.
- Duan, Z., van Kranenburg, P., Nam, J., and Rao, P. (2023). Editorial for TISMIR special collection: Cultural diversity in MIR research. Transactions of the International Society for Music Information Retrieval, 6(1), 203–205.
- Ericsson, L., Gouk, H., Loy, C. C., and Hospedales, T. M. (2022). Self‑supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine, 39(3), 42–62.
- Gerhardt, K., and Kirsch, M. (2024). Choralbücher from northern Germany c. 1800: A dataset for studies in hymnology, music culture and figured bass. Transactions of the International Society for Music Information Retrieval, 7(1), 306–315.
- Gillman, D., Kutlay, A., and Goyat, U. (2022). Teach yourself Georgian folk songs dataset: A annotated corpus of traditional vocal polyphony. In Proceedings of the 23rd International Society for Music Information Retrieval Conference, Bengaluru, India, pp. 353–360. ISMIR.
- Gulati, S. (2016). Computational Approaches for Melodic Description in Indian Art Music Corpora [PhD. thesis]. Universitat Pompeu Fabra.
- 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 ’19, New York, NY, USA, pp. 53–57. Association for Computing Machinery.
- Hsu, W.‑N., Bolte, B., Tsai, Y.‑H. H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A. (2021). Hubert: Self‑supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 3451–3460.
- Humphrey, E., Durand, S., and McFee, B. (2018). Openmic‑2018: An open dataset for multiple instrument recognition. In Proceedings of the 19th International Society for Music Information Retrieval Conference, Paris, France, pp. 438–444. ISMIR.
- Jamshidi, F., Pike, G., Das, A., and Chapman, R. (2024). Machine learning techniques in automatic music transcription: A systematic survey. arXiv:2406.15249.
- Ji, S., Yang, X., and Luo, J. (2023). A survey on deep learning for symbolic music generation: Representations, algorithms, evaluations, and challenges. ACM Computing Surveys, 56(1), 1–39.
- Lartillot, O., Johansson, M. S., Elowsson, A., Monstad, L. L., and Cyvin, M. (2023). A dataset of norwegian hardanger fiddle recordings with precise annotation of note and beat onsets. Transactions of the International Society for Music Information Retrieval, 6(1), 186–202.
- Li, B., Liu, X., Dinesh, K., Duan, Z., and Sharma, G. (2019). Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications. IEEE Transactions on Multimedia, 21(2), 522–535.
- Li, Y., Yuan, R., Zhang, G., Ma, Y., Chen, X., Yin, H., Xiao, C., Lin, C., Ragni, A., Benetos, E., Gyenge, N., Dannenberg, R. B., Liu, R., Chen, W., Xia, G., Shi, Y., Huang, W., Wang, Z., Guo, Y., and Fu, J. (2023). Mert: Acoustic music understanding model with large‑scale self‑supervised training. arXiv:2306.00107.
- Li, Y., Yuan, R., Zhang, G., Ma, Y., Lin, C., Chen, X., Ragni, A., Yin, H., Hu, Z., He, H., Benetos, E., Gyenge, N., Liu, R., and Fu, J. (2022). Map‑music2vec: A simple and effective baseline for self‑supervised music audio representation learning. arXiv:2212.02508.
- Liberman, A. M., and Mattingly, I. G. (1989). A specialization for speech perception. Science, 243(4890), 489–494.
- Lidy, T., Silla, C. N.,
Jr ., Cornelis, O., Gouyon, F., Rauber, A., Kaestner, C. A., and Koerich, A. L. (2010). On the suitability of state‑of‑the‑art music information retrieval methods for analyzing, categorizing and accessing non‑western and ethnic music collections. Signal Processing, 90(4), 1032–1048. - Ma, Y., Øland, A., Ragni, A., Del Sette, B. M., Saitis, C., Donahue, C., Lin, C., Plachouras, C., Benetos, E., Shatri, E., Morreale, F., Zhang, G., Fazekas, G., Xia, G., Zhang, H., Manco, I., Huang, J., Guinot, J., Lin, L., . . . Wang, Z. (2024). Foundation models for music: A survey. arXiv:2408.14340.
- Ma, Y., Yuan, R., Li, Y., Zhang, G., Lin, C., Chen, X., Ragni, A., Yin, H., Benetos, E., Gyenge, N., Liu, R., Xia, G., Dannenberg, R. B., Guo, Y., and Fu, J. (2023). On the effectiveness of speech self‑supervised learning for music. In Proceedings of the 24th International Society for Music Information Retrieval Conference, Milan, Italy, pp. 457–465. ISMIR.
- Maia, L., Fuentes, M., Biscainho, L., Rocamora, M., and Essid, S. (2019). Sambaset: A dataset of historical samba de enredo recordings for computational music analysis. In Proceedings of the 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, pp. 628–635. ISMIR.
- Maia, L. S., Tomaz Jr, P., Fuentes, M., Rocamora, M., Biscainho, L., Costa, M., and Cohen, S. (2018). A novel dataset of Brazilian rhythmic instruments and some experiments in computational rhythm analysis. In 2018 AES Latin American Congress of Audio Engineering, pp. 53–60. Audio Engineering Society.
- Manilow, E., Seetharaman, P., and Pardo, B. (2020). Simultaneous separation and transcription of mixtures with multiple polyphonic and percussive instruments. In ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 771–775. IEEE.
- Manilow, E., Wichern, G., Seetharaman, P., and Le Roux, J. (2019). Cutting music source separation some slakh: A dataset to study the impact of training data quality and quantity. In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 45–49. IEEE.
- Martín‑Morató, I., Harju, M., Ahokas, P., and Mesaros, A. (2023). Training sound event detection with soft labels from crowdsourced annotations. In ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE.
- Massoudieh, M. T. (2016). Polyphony in Iranian music. Translingual Discourse in Ethnomusicology, 2, 82–102.
- Méndez Méndez, A. E., Cartwright, M., Bello, J. P., and Nov, O. (2022). Eliciting confidence for improving crowdsourced audio annotations. In Proceedings of the ACM on Human‑Computer Interaction, 6(CSCW1).
- Nettl, B. (2012). Iran XI: Music. Encyclopædia Iranica, XIII(5), 474–480.
- Nikzat, B., and Repetto, R. C. (2022). KDC: An open corpus for computational research of dastgāhi music. In Proceedings of the 23rd International Society for Music Information Retrieval Conference, Bengaluru, India, pp. 321–328. ISMIR.
- Nunes, L. O., Rocamora, M., Jure, L., and Biscainho, L. W. P. (2015). Beat and downbeat tracking based on rhythmic patterns applied to the Uruguayan candombe drumming. In Proceedings of the 16th International Society for Music Information Retrieval Conference, Málaga, Spain, pp. 264–270. ISMIR.
- Papaioannou, C., Valiantzas, I., Giannakopoulos, T., Kaliakatsos‑Papakostas, M., and Potamianos, A. (2022). A dataset for Greek traditional and folk music: Lyra. arXiv:2211.11479.
- Rosenzweig, S., Scherbaum, F., Shugliashvili, D., Arifi‑Müller, V., and Müller, M. (2020). Erkomaishvili dataset: A curated corpus of traditional Georgian vocal music for computational musicology. Transactions of the International Society for Music Information Retrieval, 3(1), 31–41.
- Şentürk, S. (2016). Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman‑Turkish Makam [PhD. thesis]. Universitat Pompeu Fabra.
- Serra, X. (2014). Creating research corpora for the computational study of music: The case of the CompMusic project. In Audio Engineering Society Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society.
- Serra, X. (2017). The computational study of a musical culture through its digital traces. Acta Musicologica, 89(1), 24–44.
- Srinivasamurthy, A. (2016). A Data‑Driven Bayesian Approach to Automatic Rhythm Analysis of Indian Art Music [PhD thesis]. Universitat Pompeu Fabra.
- Thickstun, J., Harchaoui, Z., and Kakade, S. (2016). Learning features of music from scratch. arXiv:1611.09827.
- Zaker Jafari, N. (2019). History of music ensemble and instrumental accompaniment in the Islamic era of Iran until the Qajar. Journal of Fine Arts: Performing Arts & Music, 24(2), 5–16.
- Zhang, Y., Zhou, Z., Li, X., Yu, F., and Sun, M. (2022). CCOM‑HuQin: An annotated multimodal Chinese fiddle performance dataset. arXiv:2209.06496.
- Zhou, M., Xu, S., Liu, Z., Wang, Z., Yu, F., Li, W., and Han, B. (2025). CCMusic: An open and diverse database for Chinese music information retrieval research. Transactions of the International Society for Music Information Retrieval, 8(1), 22–38.
DOI: https://doi.org/10.5334/tismir.257 | Journal eISSN: 2514-3298
Language: English
Submitted on: Feb 15, 2025
Accepted on: Jun 15, 2025
Published on: Jul 28, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2025 Pouya Mohseni, Bagher BabaAli, Hooman Asadi, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.