Abstract
In this paper models and algorithms are presented for transcription of pitch and timings in polyphonic music extracts. The data are decomposed framewise into the frequency domain, where a Poisson point process model is used to write a polyphonic pitch likelihood function. From here Bayesian priors are incorporated both over time (to link successive frames) and also within frames (to model the number of notes present, their pitches, the number of harmonics for each note, and inharmonicity parameters for each note). Inference in the model is carried out via Bayesian filtering using a powerful Sequential Markov chain Monte Carlo (MCMC) algorithm that is an MCMC extension of particle filtering. Initial results with guitar music, both laboratory test data and commercial extracts, show promising levels of performance.
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References
Cemgil, A., Godsill, S.J., Peeling, P., Whiteley, N.: Bayesian statistical methods for audio and music processing. In: O’Hagan, A., West, M. (eds.) Handbook of Applied Bayesian Analysis, OUP (2010)
Davy, M., Godsill, S., Idier, J.: Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America 119(4) (April 2006)
Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (eds.): Markov Chain Monte Carlo in Practice. Chapman and Hall, Boca Raton (1996)
Godsill, S.J., Davy, M.: Bayesian computational models for inharmonicity in musical instruments. In: Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY (October 2005)
Kashino, K., Nakadai, K., Kinoshita, T., Tanaka, H.: Application of the Bayesian probability network to music scene analysis. In: Rosenthal, D.F., Okuno, H. (eds.) Computational Audio Scene Analysis, pp. 115–137. Lawrence Erlbaum Associates, Mahwah (1998)
Klapuri, A., Davy, M.: Signal processing methods for music transcription. Springer, Heidelberg (2006)
Pang, S.K., Godsill1, S.J., Li, J., Septier, F.: Sequential inference for dynamically evolving groups of objects. To appear: Barber, Cemgil, Chiappa (eds.) Inference and Learning in Dynamic Models, CUP (2009)
Peeling, P.H., Li, C., Godsill, S.J.: Poisson point process modeling for polyphonic music transcription. Journal of the Acoustical Society of America Express Letters 121(4), EL168–EL175 (2007)
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Bunch, P., Godsill, S. (2011). Transcription of Musical Audio Using Poisson Point Processes and Sequential MCMC. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K. (eds) Exploring Music Contents. CMMR 2010. Lecture Notes in Computer Science, vol 6684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23126-1_6
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DOI: https://doi.org/10.1007/978-3-642-23126-1_6
Publisher Name: Springer, Berlin, Heidelberg
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