A continuous time model for Karnatic flute music synthesis

References

• Ashtamoorthy, A., Prasad, P., Dhar, S., & Vijayasenan, D. (2018). Frequency contour modeling to synthesize natural flute renditions for Carnatic music. Proceedings of the 2018 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India. (pp.172–20). IEEE.
   Google Scholar
• Ayers, L. (2003). Synthesizing trills for the Chinese dizi. Proceedings of the International Computer Music Conference, ICM, Bangalore, India.
   Google Scholar
• Ayers, L. (2004). Synthesizing timbre tremolos and flutter tonguing on wind instruments. Proceedings of the International Computer Music Conference, ICMC, Miami, Florida, USA. (pp. 390–393).
   Google Scholar
• Ayers, L. (2005). Synthesising Chinese flutes using C sound. Organised Sound, 10(1), 37–49. https://doi.org/10.1017/S1355771805000658
   Google Scholar
• Benade, A. H. (1990). Fundamentals of musical acoustics. Dover Publications.
   Google Scholar
• Boersma, P., & Weenink, D. (2001). PRAAT, a system for doing phonetics by computer. Glot International, 5(9), 341–345. https://cir.nii.ac.jp/crid/1572261550900588928
   Google Scholar
• Conard, N., Malina, M., & Münzel, S. (2009, July). New flutes document the earliest musical tradition in Southwestern Germany. Nature, 460(7256), 737–740. https://doi.org/10.1038/nature08169
   PubMed Web of Science ®Google Scholar
• Dïaz, A. and Mendes, R. (2015). Analysis synthesis of the Andean Quena via harmonic Band Wavelet Transform. Proceedings of the Digital Audio Effects (DAFx-15), Trondheim, Norway (pp. 433–437).
   Google Scholar
• Donahue, C., McAuley, J., & Puckette, M. (2018). Adversarial audio synthesis. arXiv Preprint arXiv: 1802.04208.
   Google Scholar
• Engel, J. et al. (2015). Neural audio synthesis of musical notes with WaveNet autoencoders. Proceedings of the International Conference on Machine Learning, Sydney, Australia (pp. 1068–1077).
   Google Scholar
• Engel, J., et al. (2019). Gansynth: Adversarial neural audio synthesis. arXiv Preprint arXiv: 1902.08710.
   Google Scholar
• Hawthorne, C., et al. (2018). Enabling factorized piano music modeling and generation with the MAESTRO dataset. arXiv Preprint arXiv: 1810.12247.
   Google Scholar
• Helmholtz, H. L. (1954). On the Sensations of tone, translated by AJ Ellis. Dover Publications.
   Google Scholar
• Horner, A., & Ayers, L. (1998). Modeling acoustic wind instruments with contiguous group synthesis. Journal of the Audio Engineering Society, 46(10), 868–879.
   Web of Science ®Google Scholar
• Karaikudi Subramanian, S. GAAYAKA. https://carnatic2000.tripod.com/gaayaka6.html
   Google Scholar
• Karaikudi Subramanian, S., Wyse, L., & McGee, K. (2011). Modeling speed doubling in carnatic music. ICMC. https://lonce.org/Publications/publications/modeling-speed-doubling-in-carnatic-music.pdf
   Google Scholar
• Keefe, D. H. (1990). Woodwind air column models. The Journal of the Acoustical Society of America, 88(1), 35–51. https://doi.org/10.1121/1.399911
   Web of Science ®Google Scholar
• Kreutzer, C., Walker, J., and O’Neill, M. (2008). A parametric model for spectral sound synthesis of musical sounds. Proceedings of the 2008 International Conference on Audio, Language and Image Processing, Shanghai, China (pp. 633–637).IEEE
   Google Scholar
• Kumar, K., Kumar, R., De Boissiere, T., Gestin, L., Teoh, W. Z., Sotelo, J., & Courville, A. C. (2019). Melgan: Generative adversarial networks for conditional waveform synthesis. Advances in Neural Information Processing Systems, 32.
   Google Scholar
• Marafioti, A. et al. (2019). Adversarial generation of time-frequency features with application in audio synthesis. Proceedings of the International Conference on Machine Learning, Long Beach, California (pp. 4352–4362). PMLR.
   Google Scholar
• McIntyre, M. E., Schumacher, R. T., & Woodhouse, J. (1983). On the oscillations of musical instruments. The Journal of the Acoustical Society of America, 74(5), 1325–1345. https://doi.org/10.1121/1.390157
   Web of Science ®Google Scholar
• Polotti, P., & Evangelista, G. (2001). Analysis and synthesis of pseudo-periodic 1/f-like noise by means of wavelets with Applications to digital audio. EURASIP Journal on Advances in Signal Processing, 2001(1), 584201. https://doi.org/10.1155/S1110865701000129
   Google Scholar
• Polotti, P., & Evangelista, G. (2007). Fractal additive synthesis. IEEE Signal Processing Magazine, 24(2), 105–115. https://doi.org/10.1109/MSP.2007.323275
   Web of Science ®Google Scholar
• Ramamurthy, S. and Raghavan, M. (2013). Filter design for synthesis of musical notes: A multidimensional feature-based approach. Proceedings of the 2013 IEEE International Conference on Signal and Image Processing Applications, Melaka, Malaysia (pp. 106–111).
   Google Scholar
• Rocamora, M., Lopez, E., and Jure, L. (2009) Wind instruments synthesis toolbox for generation of music audio signals with labeled partials. Proceedings of the 2009 Brazilian Symposium on Computer Music SBCM09, Recife, Brazil (pp. 2–4).
   Google Scholar
• Scavone, G. P., & Cook, P. R. (1998). Real-time computer modeling of woodwind instruments. Proceedings of the International Symposium on Musical Acoustics (ISMA-98), Leavenworth, WA (pp. 197–202).
   Google Scholar
• Series, B. S. (2019). Recommendation ITU-R BS. 1284-2, General methods for the subjective assessment of sound quality. ITU-R Recommendation BS.
   Google Scholar
• Serra, X., et al. (1997). Musical sound modeling with sinusoids plus noise. Musical Signal Processing, 91–122.
   Google Scholar
• Serra, X., & Smith, J. Spectral modeling synthesis: A sound analysis/synthesis system based on a deterministic plus stochastic decomposition. (1990). Computer Music Journal, 14(4), 12–24. issn: 01489267, 15315169. https://doi.org/10.2307/3680788
   Web of Science ®Google Scholar
• Smith, J. O., III. (1991). Viewpoints on the history of digital synthesis. Proceedings of the International Computer Music Conference, ICMC, Quebec, Canada (pp. 1–10).
   Google Scholar
• Strong, W., & Clark, M. (1967). Synthesis of wind-instrument tones. The Journal of the Acoustical Society of America, 41(1), 39–52. https://doi.org/10.1121/1.1910327
   Web of Science ®Google Scholar
• Suyun, F. and Yibiao, Y. (2016). Improve music synthesis quality by particular harmonic spectrum interpolation based on sinusoidal model. Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China (pp. 183–186). IEEE.
   Google Scholar
• Välimäki, V., Hänninen, R., and Karjalainen, M. (1996). An improved digital waveguide model of flute-implementation Issues. Proceedings of the International Computer Music Conference, Hong Kong (pp. 1–4). Citeseer.
   Google Scholar
• Valimaki, V., Karjalainen, M., Jánosy, Z., & Laine, U. K. (1992). A real-time DSP implementation of a flute model. Proceedings of the International Conference Acoustics, Speech Signal Processing (Vol. 2, pp. 249–252). IEEE Computer Society.
   Google Scholar
• Välimäki, V., Karjalainen, M., and Laakso, T. I. (1993).Modeling of woodwind bores with finger holes. Proceedings of the International Computer Music Conference, ICMC 1993, Tokyo, Japan (pp. 32–39).
   Google Scholar
• van den Oord, A., et al. (2016). Wavenet: A generative model for raw audio. arXiv Preprint arXiv: 1609.03499.
   Google Scholar
• Verma, T. S., & Meng, T. H. (2000). Extending spectral modeling synthesis with transient modeling synthesis. Computer Music Journal, 24(2), 47–59. https://doi.org/10.1162/014892600559317
   Web of Science ®Google Scholar
• Viraraghavan, V., Gavas, R., Murthy, H., & Aravind, R. (2019). Visualizing carnatic music as projectile motion in a uniform gravitational field. Proceedings of the Workshop on Speech, Music and Mind 2019, Quebec, Canada (pp. 31–35).
   Google Scholar
• Ystad, S., & Voinier, T. (2001). A virtually real flute. Computer Music Journal, 25(2), 13–24. https://doi.org/10.1162/014892601750302552
   Web of Science ®Google Scholar