References
- 1Bello, J. P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., and Sandler, M. B. (2005). A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing, 13(5):1035–1047. DOI: 10.1109/TSA.2005.851998
- 2Bonada, J., Serra, X., Amatriain, X., and Loscos, A. (2011).
Spectral processing . In Zolzer, U., editor, DAFX: Digital Audio Effects, chapter 10, pages 393–445. John Wiley & Sons, Ltd. DOI: 10.1002/9781119991298.ch10 - 3Brossier, P., Bello, J. P., and Plumbley, M. D. (2004). Fast labelling of notes in music signals. In Proceedings of the 5th International Conference on Music Information Retrieval, Barcelona, Spain.
- 4Choi, K., Fazekas, G., Sandler, M., and Cho, K. (2017). Transfer learning for music classification and regression tasks. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
- 5Chordia, P. (2005). Segmentation and recognition of tabla strokes. In Proceedings of the 6th International Conference on Music Information Retrieval, London, U.K.
- 6Chordia, P. and Rae, A. (2008). Tabla Gyan: A system for realtime tabla recognition and resynthesis. In Proceedings of the 34th International Computer Music Conference, Belfast, Ireland.
Michigan Publishing . - 7Clayton, M. (2001). Time in Indian Music: Rhythm, Metre, and Form in North Indian Rag Performance. Oxford University Press, U.K.
- 8Clayton, M. (2020). Theory and practice of longform non-isochronous metres. Music Theory Online, 26(1). DOI: 10.30535/mto.26.1.2
- 9Courtney, D. (2013). Fundamentals of Tabla. Sur Sangeet Services.
- 10Dittmar, C. and Gartner, D. (2014). Real-time transcription and separation of drum recordings based on NMF decomposition. In Proceedings of the 17th International Conference on Digital Audio Effects, Erlangen, Germany.
- 11Gillet, O. and Richard, G. (2003). Automatic labelling of tabla signals. In Proceedings of the 4th International Conference on Music Information Retrieval, Baltimore, USA.
- 12Gillet, O. and Richard, G. (2006). ENST-Drums: An extensive audio-visual database for drum signals processing. In Proceedings of the 7th International Conference on Music Information Retrieval, Victoria, Canada.
- 13Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., and Feris, R. (2019). Spottune: Transfer learning through adaptive fine-tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA. DOI: 10.1109/CVPR.2019.00494
- 14Gupta, S., Srinivasamurthy, A., Kumar, M., Murthy, H. A., and Serra, X. (2015). Discovery of syllabic percussion patterns in tabla solo recordings. In Proceedings of the 16th International Society for Music Information Retrieval Conference, Malaga, Spain.
- 15Jacques, C. and Robel, A. (2018). Automatic drum transcription with convolutional neural networks. In Proceedings of the 21st International Conference on Digital Audio Effects, Aveiro, Portugal.
- 16Jacques, C. and Robel, A. (2019). Data augmentation for drum transcription with convolutional neural networks. In Proceedings of the 27th IEEE European Signal Processing Conference, A Coruna, Spain. DOI: 10.23919/EUSIPCO.2019.8902980
- 17Kingma, D. P. and Ba, J. (2015). Adam: A method for stochastic optimization. In Proceedings of 3rd International Conference on Learning Representations, ICLR, San Diego, USA.
- 18Narang, K. and Rao, P. (2017). Acoustic features for determining goodness of tabla strokes. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
- 19Rohit, M. A., Bhattacharjee, A., and Rao, P. (2021). Four-way classification of tabla strokes with models adapted from automatic drum transcription. In Proceedings of the 22nd International Society for Music Information Retrieval Conference, Online.
- 20Rohit, M. A. and Rao, P. (2018).
Acoustic-prosodic features of tabla bol recitation and correspondence with the tabla imitation . In Proceedings of Interspeech, Hyderabad, India. - 21Rohit, M. A. and Rao, P. (2021). Automatic stroke classification of tabla accompaniment in Hindustani vocal concert audio. The Journal of Acoustical Society of India, 48(1–2).
- 22Sarkar, R., Singh, A., Mondal, A., and Saha, S. K. (2018).
Automatic extraction and identification of bol from tabla signal . In Chaki, R., Cortesi, A., Saeed, K., and Chaki, N., editors, Advanced Computing and Systems for Security: Volume Five, pages 139–151. Springer Singapore. DOI: 10.1007/978-981-10-8180-4_9 - 23Schlüter, J. and Bock, S. (2014). Improved musical onset detection with convolutional neural networks. In Proceedings of the 39th IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy. DOI: 10.1109/ICASSP.2014.6854953
- 24Schlüter, J. and Grill, T. (2015). Exploring data augmentation for improved singing voice detection with neural networks. In Proceedings of the 16th International Society for Music Information Retrieval Conference, Malaga, Spain.
- 25Shete, S. and Deshmukh, S. (2021).
North Indian classical music tabla tala (rhythm) prediction system using machine learning . In Biswas, A., Wennekes, E., Hong, T.-P., and Wieczorkowska, A., editors, Advances in Speech and Music Technology. Springer Singapore. DOI: 10.1007/978-981-33-6881-1_16 - 26Southall, C., Stables, R., and Hockman, J. (2017a). Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
- 27Southall, C., Wu, C.-W., Lerch, A., and Hockman, J. (2017b). MDB Drums: An annotated subset of MedleyDB for automatic drum transcription. In Extended Abstracts for the Late-Breaking Demo Session of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
- 28Srinivasamurthy, A., Holzapfel, A., Ganguli, K. K., and Serra, X. (2017). Aspects of tempo and rhythmic elaboration in Hindustani music: A corpus study. Frontiers in Digital Humanities, 4:20. DOI: 10.3389/fdigh.2017.00020
- 29Virtanen, P., Gommers, R., Oliphant, T. E., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17:261–272.
- 30Vogl, R., Dorfer, M., Widmer, G., and Knees, P. (2017). Drum transcription via joint beat and drum modeling using convolutional recurrent neural networks. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
- 31Wu, C., Dittmar, C., Southall, C., Vogl, R., Widmer, G., Hockman, J., Muller, M., and Lerch, A. (2018). A review of automatic drum transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(9). DOI: 10.1109/TASLP.2018.2830113
- 32Yong, S., Choi, S., and Nam, J. (2020). PyTSMod: A Python implementation of time-scale modification algorithms. In Extended Abstracts for the Late-Breaking Demo Session of the 21st International Society for Music Information Retrieval Conference, Montreal, Canada.