SVR-based music mood classification and context-based music recommendation

ABSTRACT

With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing
          Subjects: Methodologies and techniques

General Terms:
Algorithms, Design, Experimentation, Measurement

Keywords:
classification, music mood, ontology, recommendation, support vector regression