6533b82bfe1ef96bd128dff7

RESEARCH PRODUCT

Modeling musical attributes to characterize ensemble recordings using rhythmic audio features

Olivier LartillotGerald SchullerJakob AbesserTuomas EerolaChristian Dittmar

subject

Set (abstract data type)Sound recording and reproductionMusicologyComputer scienceSpeech recognitionFeature extractionMusicalAudio signal processingcomputer.software_genrecomputer

description

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.

yearjournalcountryeditionlanguage
2011-05-012011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
https://doi.org/10.1109/icassp.2011.5946372