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Modeling musical attributes to characterize ensemble recordings using rhythmic audio features

: Abeßer, Jakob; Lartillot, Oliver; Dittmar, Christian; Eerola, Tuomas; Schuller, Gerald


IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011. Vol.1 : Prague, Czech Republic, 22 - 27 May 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-4577-0538-0 (Print)
ISBN: 978-1-4577-0539-7
ISBN: 978-1-4577-0537-3 (Online)
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <36, 2011, Prague>
Conference Paper
Fraunhofer IDMT ()
music performance analysis; music performance assessment

In this paper, we present the results of a pre-study on music performance analysis of ensemble music. Our aim is to implement a music classification system for the description of live recordings, for instance to help musicologist and musicians to analyze improvised ensemble performances. The main problem we deal with is the extraction of a suitable set of audio features from the recorded instrument tracks. Our approach is to extract rhythm-related audio features and to apply them for regression-based modeling of eight more general musical attributes. The model based on Partial Least-Squares Regression without preceding Principal Component Analysis performed best for all of the eight attributes.