Personalization of user profiles for content-based music retrieval based on relevance feedback

ABSTRACT

Numerous efforts on content-based music information retrieval have been presented in recent years. However, the object of such existing research is to retrieve a specific song from a large music database. In this research, we propose a music retrieval method which retrieves songs based on the user's musical preferences. This enables users to discover new songs which they are expected to like. Since music preferences are expected to be highly ambiguous, we propose the implementation of relevance feedback methods to improve the performance of our music information retrieval method. In order to reduce the burden of users to input learning data to the system, we also propose a method to generate user profiles based on genre preferences, and refinement of such profiles based on relevance feedback. Evaluation experiments are conducted based on a corpus of music data with user ratings. Results of these experiments prove the effectiveness of our method.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Multimedia databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Experimentation

Keywords:
music information retrieval, relevance feedback, tree-based vector quantization, user preferences