Abstract
A general harmonic model for pitch tracking of polyphonic musical time series will be introduced. Based on a model of Davy and Godsill (2002) the fundamental frequencies of polyphonic sound are estimated simultaneously. For an improvement of these results a preprocessing step was be implemented to build an extended polyphonic model.
All methods are applied on real audio data from the McGill University Master Samples (Opolko and Wapnick (1987)).
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References
DAVY, M. and GODSILL, S. J. (2002): Bayesian Harmonic Models for Musical Pitch Estima-tion and Analysis. Technical Report 431, Cambridge University Engineering Department. GILKS, W. R., RICHARDSON, S. and SPIEGELHALTER D. J. (1996): Markov Chain Monte Carlo in Practice, Chapman & Hall.
OPOLKO, F. and WAPNICK, J. (1987): McGill University Master Samples [Compact disc]: Montreal, Quebec: McGill University.
SOMMER K. and WEIHS C. (2006): Using MCMC as a stochastic optimization procedure for music time series. In: V. Batagelj, H.H. Bock, A. Ferligoj, and A. Ziberna (Eds.): Data Science and Classifiction , Springer, Heidelberg, 307-314.
SOMMER K. and WEIHS C. (2007): Using MCMC as a stochastic optimization procedure for monophonic and polyphonic sound. In: R. Decker and H. Lenz (Eds.): Advances in Data Analysis, Springer, Heidelberg, 645-652.
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Sommer, K., Weihs, C. (2008). A Comparative Study on Polyphonic Musical Time Series Using MCMC Methods. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_34
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DOI: https://doi.org/10.1007/978-3-540-78246-9_34
Publisher Name: Springer, Berlin, Heidelberg
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