CompositeMap: a novel music similarity measure for personalized multimodal music search

ABSTRACT

How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems in music information retrieval. This paper demonstrates a novel multimodal and adaptive music similarity measure (CompositeMap) with its application in a personalized multimodal music search system. CompositeMap can effectively combine music properties from different aspects into compact signatures via supervised learning, which lays the foundation for effective and efficient music search. In addition, an incremental Locality Sensitive Hashing algorithm is developed to support more efficient search processes. Experimental results based on two large music collections reveal various advantages in effectiveness, efficiency, adaptiveness, and scalability of the proposed music similarity measure and the music search system.

REFERENCES

• 1: A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS'06, 2006.
• 2: S. J. Downie. The music information retrieval evaluation exchange (2005 -- 007): A window into music information retrieval research. Acoustical Science and Technology, 2008.
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• 4: D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. Towards musical query-by-semantic-description using the cal500 data set. In Proc. of ACM SIGIR, 2007.
• 5: B. Zhang, J. Shen, Q. Xiang, and Y. Wang. Compositemap: A novel framework for music similarity measure. In Proc. of ACM SIGIR, 2009.

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing
          Subjects: Systems

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
music, personalization, search, similarity measure