Modeling musical attributes to characterize ensemble recordings using rhythmic audio features

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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Jakob Abesser, Olivier Lartillot, Christian Dittmar, Tuomas Eerola, Gerald Schuller
• Keywords: Audio Features, ICASSP 2011, Music Performance Analysis, Partial Least-Squares Regression, Signal Processing