Logo image
Back
Modeling musical rhythmatscale with the music Genome project
Conference proceeding

Modeling musical rhythmatscale with the music Genome project

Matthew Prockup, Andreas F Ehmann, Fabien Gouyon, Erik M Schmidt and Youngmoo E Kim
2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp 1-5
Oct 2015
DOI: https://doi.org/10.1109/WASPAA.2015.7336891

Abstract

audio Bioinformatics Context feature engineering Genomics large-scale machine learning Multiple signal classification music information retrieval Rhythm signal processing Transforms
Musical meter and attributes of the rhythmic feel such as swing, syncopation, and danceability are crucial when defining musical style. However, they have attracted relatively little attention from the Music Information Retrieval (MIR) community and, when addressed, have proven difficult to model from music audio signals. In this paper, we propose a number of audio features for modeling meter and rhythmic feel. These features are first evaluated and compared to timbral features in the common task of ballroom genre classification. These features are then used to learn individual models for a total of nine rhythmic attributes covering meter and feel using an industrial-sized corpus of over one million examples labeled by experts from Pandora® Internet Radio's Music Genome Project®. Linear models are shown to be powerful, representing these attributes with high accuracy at scale.

Metrics

18 Record Views
11 citations in Scopus

Details

Logo image