CompositeMap: a novel framework for music similarity measure

ABSTRACT

With the continuing advances in data storage and communication technology, there has been an explosive growth of music information from different application domains. As an effective technique for organizing, browsing, and searching large data collections, music information retrieval is attracting more and more attention. How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems. In this paper, we introduce a novel framework based on a multimodal and adaptive similarity measure for various applications. Distinguished from previous approaches, our system can effectively combine music properties from different aspects into a compact signature via supervised learning. In addition, an incremental Locality Sensitive Hashing algorithm has been developed to support efficient retrieval processes with different kinds of queries. Experimental results based on two large music collections reveal various advantages of the proposed framework including effectiveness, efficiency, adaptiveness, and scalability.

REFERENCES

• 1: http://www.last.fm.
• 2: http://www.YouTube.com.
• 3: Alexandr Andoni , Piotr Indyk, Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions, Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science, p.459-468, October 21-24, 2006 [doi>10.1109/FOCS.2006.49]
• 4: Jon Louis Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, v.18 n.9, p.509-517, Sept. 1975 [doi>10.1145/361002.361007]
• 5: Adam Berenzweig , Beth Logan , Daniel P. W. Ellis , Brian P. W. Whitman, A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures, Computer Music Journal, v.28 n.2, p.63-76, June 2004 [doi>10.1162/014892604323112257]
• 6: M. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney. Content-based music information retrieval: current directions and future challenges. Proc. of the IEEE, 2008.
• 7: J. Stephen Downie, The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future, Computer Music Journal, v.28 n.2, p.12-23, June 2004 [doi>10.1162/014892604323112211]
• 8: S.J. Downie. The music information retrieval evaluation exchange (2005-007): A window into music information retrieval research. Acoustical Science and Technology, 2008.
• 9: Tzu-Kuo Huang , Ruby C. Weng , Chih-Jen Lin, Generalized Bradley-Terry Models and Multi-Class Probability Estimates, The Journal of Machine Learning Research, 7, p.85-115, 12/1/2006
• 10: B. Logan. Mel frequency cepstral coe±cients for music modeling. In Proc. of the ISMIR, 2000.
• 11: B. Logan and A. Salomon. A music similarity function based on signal analysis. In Proc. of IEEE ICME, 2001.
• 12: L. Lu, D. Liu, and H. Zhang. Automatic mood detection and tracking of music audio signals. IEEE Trans. Acoust., Speech, Signal, 2006.
• 13: Christopher D. Manning , Prabhakar Raghavan , Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press, New York, NY, 2008
• 14: Wing W. Y. Ng , Daniel S. Yeung , Michael Firth , Eric C. C. Tsang , Xi-Zhao Wang, Feature selection using localized generalization error for supervised classification problems using RBFNN, Pattern Recognition, v.41 n.12, p.3706-3719, December, 2008 [doi>10.1016/j.patcog.2008.05.004]
• 15: J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 2000.
• 16: Shai Shalev-Shwartz , Nathan Srebro, SVM optimization: inverse dependence on training set size, Proceedings of the 25th international conference on Machine learning, p.928-935, July 05-09, 2008, Helsinki, Finland [doi>10.1145/1390156.1390273]
• 17: Douglas Turnbull , Luke Barrington , David Torres , Gert Lanckriet, Towards musical query-by-semantic-description using the CAL500 data set, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277817]
• 18: George Tzanetakis , Perry Cook, MARSYAS: a framework for audio analysis, Organised Sound, v.4 n.3, p.169-175, December 1999 [doi>10.1017/S1355771800003071]
• 19: G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Trans. on Speech and Audio Proc., 2002.
• 20: Jinhee You , Sanghyun Park , Inbum Kim, An efficient frequent melody indexing method to improve the performance of query-by-humming systems, Journal of Information Science, v.34 n.6, p.777-798, December 2008 [doi>10.1177/0165551507087712]

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, Design, Experimentation, Human Factors

Keywords:
browsing, music, personalization, recommendation, search, similarity measure