References
- 1Atlı, H., Uyar, B., Şentürk, S., Bozkurt, B., and Serra, X. (2014). Audio feature extraction for exploring Turkish Makam music. In Proceedings of the International Conference on Audio Technologies for Music and Media (ATMM), Ankara, Turkey.
- 2Benetos, E., Dixon, S., Duan, Z., and Ewert, S. (2018). Automatic music transcription: An overview. IEEE Signal Processing Magazine, 36(1): 20–30. DOI: 10.1109/MSP.2018.2869928
- 3Bittner, R., and Bosch, J. (2019). Generalized metrics for single-F0 estimation evaluation. In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), pages 738–745.
- 4Bittner, R., McFee, B., Salamon, J., Li, P., and Bello, J. (2017). Deep salience representations for F0 estimation in polyphonic music. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 63–70.
- 5Bittner, R., Salamon, J., Tierney, M., Mauch, M., Cannam, C., and Bello, J. (2014). MedleyDB: A multitrack dataset for annotation-intensive MIR research. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), pages 155–160.
- 6Bruderer, M. (2008). Perception and Modeling of Segment Boundaries in Popular Music. PhD thesis, J.F. Schouten School for User-System Interaction Research, Technische Universiteit Eindhoven, The Netherlands.
- 7Deliege, I. (1987). Grouping conditions in listening to music: An approach to Lerdahl & Jackendoff’s grouping preference rules. Music Perception, 4(4): 325–359. DOI: 10.2307/40285378
- 8Deutsch, D. (1982).
Grouping mechanisms in music . In The Psychology of Music, pages 99–134. Academic Press. DOI: 10.1016/B978-0-12-213562-0.50008-5 - 9Durrieu, J., Richard, G., David, B., and Fevotte, C. (2010). Source/filter model for unsupervised main melody extraction from polyphonic audio signals. IEEE Transactions on Audio, Speech and Language Processing (TASLP), pages 564–575. DOI: 10.1109/TASL.2010.2041114
- 10Eremenko, V., Demirel, E., Bozkurt, B., and Serra, X. (2018). Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pages 483–490.
- 11Ganguli, K. K., Gulati, S., Serra, X., and Rao, P. (2016). Data-driven exploration of melodic structures in Hindustani music. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pages 605–611.
- 12Goto, M., Hashiguchi, H., Nishimura, T., and Oka, R. (2002). RWC Music Database: Popular, classical, and jazz music databases. In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR), pages 13–17.
- 13Gulati, S., Serrà, J., Ishwar, V., and Serra, X. (2014). Mining melodic patterns in large audio collections of Indian Art Music. In Proceedings of the International Conference on Signal Image Technology and Internet Based Systems, pages 264–271. DOI: 10.1109/SITIS.2014.73
- 14Gulati, S., Serrà, J., Ishwar, V., and Serra, X. (2016). Discovering raga motifs by characterizing communities in networks of melodic patterns. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 286–290. DOI: 10.1109/ICASSP.2016.7471682
- 15Hennequin, R., Khlif, A., Voituret, F., and Moussallam, M. (2020). Spleeter: A fast and efficient music source separation tool with pre-trained models. Journal of Open Source Software, pages 1–4. DOI: 10.21105/joss.02154
- 16Hsieh, T., Su, L., and Yang, Y. (2019). A streamlined Encoder/Decoder architecture for melody extraction. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 156–160. DOI: 10.1109/ICASSP.2019.8682389
- 17Hsu, C., and Jang, J. (2010). On the improvement of singing voice separation for monaural recordings using the MIR-1K dataset. IEEE Transactions on Audio, Speech, and Language Processing (TASLP), pages 310–319. DOI: 10.1109/TASL.2009.2026503
- 18Ishwar, V., Dutta, S., Bellur, A., and Murthy, H. A. (2013). Motif spotting in an alapana in Carnatic music. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), pages 499–504.
- 19Kassebaum, G. R. (2000).
Karnatak raga . Arnold, A., editor, The Garland Encyclopaedia of World Music, pages 89–109. Garland, New York. DOI: 10.1080/09298215.2013.866145 - 20Koduri, G. K., Ishwar, V., Serrà, J., and Serra, X. (2014). Intonation analysis of rāgas in Carnatic music. Journal of New Music Research, pages 73–94.
- 21Krishna, T. M., and Ishwar, V. (2012). Carnatic music: Svara, gamaka, motif and raga identity. In Serra, X., Rao, P., Murthy, H., and Bozkurt, B., editors, Proceedings of the 2nd CompMusic Workshop, pages 12–18.
- 22Krishnaswamy, A. (2004). Melodic atoms for transcribing Carnatic music. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR).
- 23Kum, S., and Nam, J. (2019). Joint detection and classification of singing voice melody using convolutional recurrent neural networks. Applied Sciences, 9(7). DOI: 10.3390/app9071324
- 24Kum, S., Oh, C., and Nam, J. (2016). Melody extraction on vocal segments using multi-column deep neural networks. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pages 819–825.
- 25LabROSA. (2005). MIREX05 and ADC2004. Retrieved September 1, 2021 from
https://labrosa.ee.columbia.edu/projects/melody/ . - 26Maher, R., and Beauchamp, J. (1994). Fundamental frequency estimation of musical signals using a two-way mismatch procedure. Journal of The Acoustical Society of America, 95. DOI: 10.1121/1.408685
- 27McFee, B., Nieto, O., Farbood, M. M., and Bello, J. P. (2017). Evaluating hierarchical structure in music annotations. Frontiers in Psychology, 8: 1337. DOI: 10.3389/fpsyg.2017.01337
- 28Morris, R. (2011). Tana varnams: An entry into rāga delineation in Carnatic music. Analytical Approaches to World Music, 1(1): 1–27.
- 29Nieto, O. (2015). Discovering Structure in Music: Automatic Approaches and Perceptual Evaluations. PhD thesis, New York University.
- 30Nuttall, T., Plaja-Roglans, G., Pearson, L., and Serra, X. (2021). The matrix profile for motif discovery in audio-an example application in Carnatic music. In Proceedings of the 15th International Symposium on Computer Music Multidisciplinary Research (CMMR), pages 109–118.
- 31Pearson, L. (2016). Coarticulation and gesture: An analysis of melodic movement in South Indian raga performance. Music Analysis, 35(3): 280–313. DOI: 10.1111/musa.12071
- 32Pearson, L. (2021).
“Improvisation” in play: A view through South Indian music practices . In The Routledge Handbook of Philosophy and Improvisation in the Arts, pages 446–461. Routledge, Abingdon. DOI: 10.4324/9781003179443-35 - 33Popescu, T., Widdess, R., and Rohrmeier, M. (2021). Western listeners detect boundary hierarchy in Indian music: A segmentation study. Scientific Reports, 11(1): 1–14. DOI: 10.1038/s41598-021-82629-y
- 34Quinoñero-Candela, J., Sugiyama, M., Lawrence, N., and Schwaighofer, A. (2009). Dataset Shift in Machine Learning. MIT Press. DOI: 10.7551/mitpress/9780262170055.001.0001
- 35Ramanathan, N. (2004).
Sargam and musical conception in Karnataka system . In Sargam as a Musical Material. Dr. Prabha Atrre Foundation, Pu. La. Deshpande Maharashtra Kala Academy, Mumbai. - 36Ranjani, H. G., Paramashivan, D., and Sreenivas, T. V. (2017). Quantized melodic contours in Indian Art Music perception: Application to transcription. In Proceedings of the 18th International Society for Music Information Retrieval Conference, (ISMIR), pages 174–180.
- 37Ranjani, H. G., Srinivasamurthy, A., Paramashivan, D., and Sreenivas, T. V. (2019). A compact pitch and time representation for melodic contours in Indian Art Music. The Journal of the Acoustical Society of America, 145(1): 597–603. DOI: 10.1121/1.5087277
- 38Rao, P., Ross, J. C., Ganguli, K. K., Pandit, V., Ishwar, V., Bellur, A., and Murthy, H. A. (2014). Classification of melodic motifs in raga music with timeseries matching. Journal of New Music Research, 43(1): 115–131.
- 39Rao, V., and Rao, P. (2010). Vocal melody extraction in the presence of pitched accompaniment in polyphonic music. IEEE Transactions on Audio, Speech and Language Processing (TASLP), 18(8): 2145–2154. DOI: 10.1080/09298215.2013.873470
- 40Salamon, J., Bittner, R., Bonada, J., Bosch, J., Gómez, E., and Bello, J. (2017). An analysis/synthesis framework for automatic F0 annotation of multitrack datasets. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 71–78. DOI: 10.1109/TASL.2010.2042124
- 41Salamon, J., and Gomez, E. (2012). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech and Language Processing (TASLP), pages 1759–1770. DOI: 10.1109/TASL.2012.2188515
- 42Salamon, J., Gulati, S., and Serra, X. (2012). A multipitch approach to tonic identification in Indian Classical music. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), pages 499–504.
- 43Serra, X. (2014). Creating research corpora for the computational study of music: The case of the CompMusic project. In Audio Engineering Society International Conference, pages 1–9.
- 44Serra, X., Serra, F., and Gulati, S. (2015).
sms-tools . GitHub (https://github.com/MTG/sms-tools ). - 45Serra, X., and Smith, J. (1990). Spectral modeling synthesis: A sound analysis/synthesis based on a deterministic plus stochastic decomposition. Computer Music Journal, 14: 12–24. DOI: 10.2307/3680788
- 46Sloetjes, H., and Wittenburg, P. (2008). Annotation by category-ELAN and ISO DCR. In Proceedings of the 6th international Conference on Language Resources and Evaluation (LREC).
- 47Srinivasamurthy, A., Gulati, S., Repetto, R., and Serra, X. (2020). Saraga: Open datasets for research on Indian Art Music. Empirical Musicology Review. DOI: 10.18061/emr.v16i1.7641
- 48Su, L., and Yang, Y. (2015). Combining spectral and temporal representations for multipitch estimation of polyphonic music. IEEE Transactions on Audio, Speech and Language Processing (TASLP), pages 1600–1612. DOI: 10.1109/TASLP.2015.2442411
- 49Tzanetakis, G. (2014). Computational ethnomusicology: A music information retrieval perspective. In Proceedings of the 40th International Computer Music Conference, pages 112–117.
- 50Van Walstijn, M., Bridges, J., and Mehes, S. (2016). A real-time synthesis oriented tanpura model. In Proceedings of the 19th International Conference on Digital Audio Effects (DAFx), pages 175–182.
- 51Venkataraman, M., Boominathan, P., and Nallamuthu, A. (2020). Frequency range measures in Carnatic singers. Journal of Voice. DOI: 10.1016/j.jvoice.2020.08.022
- 52Viraraghavan, V. S., Aravind, R., and Murthy, H. A. (2017). A statistical analysis of gamakas in Carnatic music. In Proceedings of the 18th International Society for Music Information Retrieval Conference, (ISMIR), pages 243–249.
- 53Viswanathan, T. (1977). The analysis of rāga ālāpana in South Indian music. Asian Music, 9(1): 13–71. DOI: 10.2307/833817
- 54Yeh, C.-C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H. A., Silva, D. F., Mueen, A., and Keogh, E. (2016). Matrix profile i: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pages 1317–1322. DOI: 10.1109/ICDM.2016.0179
- 55Yu, S., Sun, X., Yu, Y., and Li, W. (2021). Frequencytemporal attention network for singing melody extraction. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 251–255. DOI: 10.1109/ICASSP39728.2021.9413444