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)).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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.
WEIHS, C. and LIGGES, U. (2006): Parameter Optimization in Automatic Transcription of Music. In: Spiliopoulou, M., Kruse, R., Nürnberger, A., Borgelt, C. and Gaul, W. (eds.): From Data and Information Analysis to Knowledge Engineering. Springer, Berlin, 740 -747.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-78246-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)