Müzik türlerinin siniflandirilmasinda benzer kesişim bilgileri uygulamalari

Translated title of the contribution: Inter genre similarity modeling for automatic music genre classification

Ulaş Baǧci*, Engin Erzin

*Corresponding author for this work

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

3 Scopus citations

Abstract

Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modeling (IGS) to improve the automatic music genre classification performance. Intergenre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modeled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modeling is further improved with iterative IGS modeling and score modeling for IGS elimination. Experimental results with promising classification improvements are provided.

Translated title of the contributionInter genre similarity modeling for automatic music genre classification
Original languageTurkish
Title of host publication2006 IEEE 14th Signal Processing and Communications Applications Conference
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE 14th Signal Processing and Communications Applications - Antalya, Turkey
Duration: Apr 17 2006Apr 19 2006

Publication series

Name2006 IEEE 14th Signal Processing and Communications Applications Conference
Volume2006

Conference

Conference2006 IEEE 14th Signal Processing and Communications Applications
Country/TerritoryTurkey
CityAntalya
Period4/17/064/19/06

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

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