Abstract
Based on a model of Davy and Godsill (2002) we describe a general model for time series from monophonic musical sound to estimate the pitch. The model is a hierarchical Bayes Model which will be estimated with MCMC methods. All the parameters and their prior distributions are motivated individually. For parameter estimation an MCMC based stochastic optimization is introduced. In a simulation study it will be looked for the best implementation of the optimization procedure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
DAVY, M. and GODSILL, S.J. (2002): Bayesian Harmonic Models for Musical Pitch Estimation and Analysis. Technical Report 431, Cambridge University Engineering Department.
LIGGES, U., WEIHS, C., HASSE-BECKER, P. (2002): Detection of Locally Stationary Segments in Time Series. In: Härdle, W., Rönz, B. (Hrsg.): COMPSTAT 2002-Proceedings in Computational Statistics-15th Symposium held in Berlin, Germany. Heidelberg: Physica, 285–290.
McGill University Master Samples. McGill University, Quebec, Canada. http://www.music.mcgill.ca/resources/mums/html/index.htm
ROSSIGNOL, S., RODET, X., DEPALLE, P., SOUMAGNE, J. and COLLETTE, J.-L.(1999): Vibrato: Detection, Estimation, Extraction, Modification. Digital Audio Effects Workshop (DAFx’99).
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
© 2006 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Sommer, K., Weihs, C. (2006). Using MCMC as a Stochastic Optimization Procedure for Musical Time Series. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_33
Download citation
DOI: https://doi.org/10.1007/3-540-34416-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34415-5
Online ISBN: 978-3-540-34416-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)