Abstract
Based on former work on automatic transcription of musical time series into sheet music (Ligges et al. (2002), Weihs and Ligges (2003, 2005)) in this paper parameters of the transcription algorithm are optimized for various real singers. Moreover, the parameters of various artificial singer models derived from the models of Rossignol et al. (1999) and Davy and Godsill (2002) are estimated. In both cases, optimization is carried out by the Nelder-Mead (1965) search algorithm. In the modelling case a hierarchical Bayes extension is estimated by WinBUGS (Spiegelhalter et al. (2004)) as well. In all cases, optimal parameters are compared to heuristic estimates from our former standard method.
This work has been supported by the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 475.
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References
CANO, P., LOSCOS, A., and BONADA, J. (1999): Score-Performance Matching using HMMs. In: Proceedings of the International Computer Music Conference. Beijing, China.
DAVY, M. and GODSILL, S.J. (2002): Bayesian Harmonic Models for Musical Pitch Estimation and Analysis. Technical Report 431, Cambridge University Engineering Department.
LESAFFRE, M., TANGHE, K., MARTENS, G., MOELANTS, D., LEMAN, M., DE BAETS, B., DE MEYER, H., and MARTENS, J.-P. (2003): The MAMI Query-By-Voice Experiment: Collecting and annotating vocal queries for music information retrieval. In: Proceedings of the International Conference on Music Information Retrieval. Baltimore, Maryland, USA, October 26–30.
LIGGES, U., WEIHS, C., and HASSE-BECKER, P. (2002): Detection of Locally Stationary Segments in Time Series. In: W. Härdle and B. Rönz (Eds.): COMPSTAT 2002 — Proceedings in Computational Statistics — 15th Symposium held in Berlin, Germany, Physika, Heidelberg, 285–290.
NELDER, J.A. and MEAD, R. (1965): A Simplex Method for Function Minimization. The Computer Journal, 7, 308–313.
R DEVELOPMENT CORE TEAM (2004): R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
RAPHAEL, C. (2001): A Probabilistic Expert System for Automatic Musical Accompaniment. Journal of Computational and Graphical Statistics, 10, 487–512.
ROSSIGNOL, S., RODET, X., DEPALLE, P., SOUMAGNE, J., and COLLETTE, J.-L. (1999): Vibrato: Detection, Estimation, Extraction, Modification. Digital Audio Effects Workshop (DAFx’99).
SPIEGELHALTER, D.J., THOMAS, A., BEST, N.G. and LUNN, D. (2004): Win-BUGS: User Manual. Version 2.0, Cambridge: Medical Research Council Biostatistics Unit.
WEIHS, C. and LIGGES, U. (2003): Automatic Transcription of Singing Performances. Bulletin of the International Statistical Institute, 54th Session, Proceedings, Volume LX, Book 2, 507–510.
WEIHS, C. and LIGGES, U. (2005): From Local to Global Analysis of Musical Time Series. In: K. Morik, A. Siebes and J.-F. Boulicault (Eds.): Local Pattern Detection, Springer Lecture Notes in Artificial Intelligence, 3539, Springer, Berlin, 217–231.
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Weihs, C., Ligges, U. (2006). Parameter Optimization in Automatic Transcription of Music. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_91
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