Music genre classification using multiscale scattering and sparse representations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

An effective music genre classication approach is proposed that combines the translation-invariance and deformation-robustness property of scattering coefficients and the discriminative power of sparse representation-based classifiers. We argue that these two approaches to feature selection and classification complement each other in reducing the in-class variability of data, and this should lead to enhanced performance. Our results show clear improvement over a variety of previous approaches. A music genre classication accuracy of approximately 91.2% on the GTZAN database is reported.

Original languageEnglish (US)
Title of host publication2013 47th Annual Conference on Information Sciences and Systems, CISS 2013
DOIs
StatePublished - Aug 20 2013
Event2013 47th Annual Conference on Information Sciences and Systems, CISS 2013 - Baltimore, MD, United States
Duration: Mar 20 2013Mar 22 2013

Publication series

Name2013 47th Annual Conference on Information Sciences and Systems, CISS 2013

Other

Other2013 47th Annual Conference on Information Sciences and Systems, CISS 2013
CountryUnited States
CityBaltimore, MD
Period3/20/133/22/13

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Chen, X., & Ramadge, P. J. (2013). Music genre classification using multiscale scattering and sparse representations. In 2013 47th Annual Conference on Information Sciences and Systems, CISS 2013 [6552324] (2013 47th Annual Conference on Information Sciences and Systems, CISS 2013). https://doi.org/10.1109/CISS.2013.6552324