Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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Music Mood Classification based on a New Feature Reduction Method and Modular Neural Network

단위 신경망과 특징벡터 차원 축소 기반의 음악 분위기 자동판별

  • 송민균 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 김현수 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 문창배 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 김병만 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 오득환 (금오공과대학교 컴퓨터소프트웨어공학과)
  • Received : 2013.05.06
  • Accepted : 2013.07.25
  • Published : 2013.08.31
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Abstract

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. The experiments show that the feature reduction methods suggested in this paper have better performance than that of the conventional dimension reduction methods, R-Square and PCA. As performance improvement by feature reduction only is subject to limit, modular neural network is used as another method to improve the performance. The experiments show that the method also improves performance effectively.

본 논문에서는 개인화된 분위기 분류 모델 대신에 대중의 분위기 분류 모델을 제안한다. 분위기 판별 성능을 개선하기 위해 두 가지 접근 방법을 선택하였는데, 그 첫 번째가 표준편차에 기초한 특징축소이다. 이는 음악의 특징을 추출하기 위해 사용하는 MIRtoolbox에서 추출되는 391개의 특징들을 모두 사용할 경우의 성능 저하 문제를 해결하기 위한 방법이다. 실험결과, 본 논문에서 제안한 특징축소 방법이 기존의 차원 축소 방법인 R-Square와 PCA보다 성능이 좋음을 확인할 수 있었다. 그리고 특징축소 방법만으로는 성능 개선에 한계가 있어 두 번째 개선방법으로 단위 신경망을 사용하여 추가의 성능 개선을 시도하였다. 실험결과 이 역시 유효한 성능 개선이 이루어짐을 확인할 수 있었다.

Keywords

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