Inter genre similarity modelling for automatic music genre classification

Ulaş Baǧci*, Engin Erzin

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modelled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modelling is further improved with iterative IGS modelling(IIGS) and score modelling for IGS elimination( SMIGS). Experimental results with promising classification improvements are provided.

Original languageEnglish (US)
Pages153-156
Number of pages4
StatePublished - 2013
Externally publishedYes
Event9th International Conference on Digital Audio Effects, DAFx 2006 - Montreal, QC, Canada
Duration: Sep 18 2006Sep 20 2006

Conference

Conference9th International Conference on Digital Audio Effects, DAFx 2006
CountryCanada
CityMontreal, QC
Period9/18/069/20/06

ASJC Scopus subject areas

  • Signal Processing

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