Обзор графических вероятностных моделей гармонии для анализа музыкальных произведений
Ключевые слова:
информационный поиск музыки, рекомендательные системы, гармония, оценка схожести, графические модели, вероятностные модели, байесовский вывод, марковские цепи, скрытые марковские моделиАннотация
Цель статьи — познакомить читателя с современным состоянием дел в области автоматического анализа музыкальной гармонии. Мотивацией для исследований в этой области может являться создание автоматических систем рекомендации музыки, ориентированных на содержание (наподобие Pandora, но без ручного труда экспертов-музыковедов). Основное внимание уделено графическим вероятностным моделям как одному из наиболее перспективных подходов, но описываются и альтернативные методы. Рассмотрены работы, использующие марковские цепи, скрытые марковские модели, многоуровневые графические модели. Приведены как работы, моделирующие только гармонию — последовательности аккордов, в некоторых случаях и тональность, — так и работы, включающие в себя информацию о структуре анализируемого произведения (ритмической, голосовой).Литература
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Amazon.com: Online Shopping for Electronics, Apparel, Computers, Books, DVDs & more. URL: http://amazon.com
Last.fm - Listen to internet radio and the largest music catalogue online. URL: http://last.fm
Pandora Radio — Listen to Free Internet Audio, Find New Music. URL: http://pandora.com
Apple - iTunes - Everything you need to be entertained. URL: http://www.apple.com/itunes/
Netflix - Watch TV Shows Online, Watch Movies Online. URL: https://www.netflix.com/
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Cemgil A.T., Kappen B., Desain P., Honing H On tempo tracking: Tempogram representa-tion and Kalman filtering // Journal of New Music Research. 2000. 29. Pp. 259–273
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Davy M Multiple fundamental frequency estimation based on generative models // In A.P. Klapuri & M. Davy (Eds.), Signal Processing Methods for Music Transcription (pp. 203–227). New York: Springer, 2006. 452 pp
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Doornbusch P Computer Sound Synthesis in 1951 — The Music of CSIRAC // Computer Music Journal. 2004. Volume 28 Issue 1. URL: http://www.mitpressjournals.org/doi/pdf/10.1162/014892604322970616
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Godsill S.J., Rayner P.J.W Digital Audio Restoration — A Statistical Model-Based Ap-proach. Springer-Verlag, 1998. 328 pp
Grubb L A Probabilistic Method for Tracking a Vocalist. Ph.D. thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1998. 258 pp
Amazon.com: Online Shopping for Electronics, Apparel, Computers, Books, DVDs & more. URL: http://amazon.com
Last.fm - Listen to internet radio and the largest music catalogue online. URL: http://last.fm
Pandora Radio — Listen to Free Internet Audio, Find New Music. URL: http://pandora.com
Apple - iTunes - Everything you need to be entertained. URL: http://www.apple.com/itunes/
Netflix - Watch TV Shows Online, Watch Movies Online. URL: https://www.netflix.com/
Hunter D.R, Lange K A Tutorial on MM Algorithms // The American Statistician. 2004. № 58. Pp. 30–37
Hunter D.R MM algorithms for generalized Bradley-Terry models // The Annals of Statis-tics. 2004. № 32 (1). Pp. 384–406
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Jackman S Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo / American Journal of Political Science. 2000. № 44(2). Pp. 375–404
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Jordan M.I., Ghahramani Z., Jaakkola T.S., Saul L.K An Introduction to Variational Methods for Graphical Models / Machine Learning. 1998. № 37. Pp. 183–233
Joyce J Pandora and the Music Genome Project / Scientific Computing. 2006. № 23 (10). Pp. 14, 40–41
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Kirlin P., Utgoff P VOISE: Learning to segregate voices in explicit and implicit polyphony // In J. Reiss & G. Wiggins (Eds.), Proceedings of the Sixth International Conference on Music Information Retrieval (ISMIR 2005) (London, UK, September 11–15, 2005). London, UK: University of London, 2005. Pp. 552–557
Klapuri A.P Automatic music transcription as we know it today / Journal of New Music Research. 2004. № 33. Pp. 269–282
Klapuri A.P., Davy M Processing Methods for Music Transcription. New York: Springer, 2006. 452 pp
Kostka S., Payne D Workbook for Tonal Harmony. New York: McGraw Hill, 1995.
Kröger P., Passos A., Sampaio Marcos, de Cidra Givaldo Rameau: A System For Auto-matic Harmonic Analysis // In Proceedings of ICMC 2008 (Belfast, UK). URL: http://quod.lib.umich.edu/i/icmc/bbp2372.2008.077?view=toc
Lee K., Slaney M A Unified System for Chord Transcription and Key Extraction Using Hidden Markov Models // Proceedings of the Fourth International Conference on Music Infor-mation Retrieval (ISMIR 2003) (Baltimore, Maryland (USA), 26-30 October 2003). Pp. 245–250
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Longuet-Higgins H.C Mental Processes: Studies in Cognitive Science. MIT Press, Cam-bridge, 1987. 424 pp
Longuet-Higgins H.C The perception of melodies // Nature. 1976. № 263. Pp. 646–653
Longuet-Higgins H.C., Lee C Perception of musical rhythms // Perception. 1982. № 11(2). Pp. 115–128
Mackay D.J Information Theory, Inference, and Learning Algorithms. Cambridge Univer-sity Press, 2003. 640 pp
Marques G., Lopes M., Sordo M., Langlois T., Gouyon F Additional Evidence That Com-mon Low-Level Features of Individual Audio Frames Are Not Representative of Music Genre // Proceedings of the Eleventh International Society for Music Information Retrieval Confer-ence (ISMIR 2010) (Utrecht, Netherlands, August 9–13, 2010). URL: http://www.mtg.upf.edu/system/files/publications/20.pdf
Mauch M., Dixon D., Harte Ch., Casey M., Fields B Discovering Chord Idioms Through Beatles and Real Book Songs // Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007) (Vienna, Austria, September 23-27) Pp. 255–258
Minka T A family of algorithms for approximate Bayesian inference. PhD thesis, Massa-chusetts Institute of Technology, 2001. 75 pp
Minka T Expectation propagation for approximate Bayesian inference // In Breese, Jack S. and Koller, Daphne (eds.), Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (Seattle, WA, August 2–5, 2001). Massachusetts: Morgan Kaufmann, 2001. Pp. 362–369
Mont-Reynaud B Problem-Solving Strategies in a Music Transcription System // In Pro-ceedings of the 1985 International Joint Conference on Artificial Intelligence (Los Angeles, California, August 18–23, 1985). Pp. 916–918
Moorer J On the transcription of musical sound by computer // Computer Music Jour-nal. 1977. № 1(4). Pp. 32–38
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Опубликован
2011-06-01
Как цитировать
Балтийский, И. А., & Николенко, С. И. (2011). Обзор графических вероятностных моделей гармонии для анализа музыкальных произведений. Труды СПИИРАН, 2(17), 174-196. https://doi.org/10.15622/sp.17.9
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