Stability Improvement of Indirect Matching for Music Information Retrieval

This paper reports improvement of indirect matching, which is a fast CBMIR (content-based music information retrieval) framework proposed in our previous study. Indirect matching achieves fast retrieval by combining offline search with representative queries and online quick similarity estimation based on the results of the offline search. We have found that the retrieval accuracy of indirect matching decreases when representative queries have little variation. This paper proposes a method for selecting representative queries having wide variation. To ensure wide variation between representative queries, the proposed method combines MDS (multi-dimensional scaling) and Ward's clustering. Experimental results have shown that the retrieval accuracy of indirect matching can be stabilized by the proposed method.

[1]  Masataka Goto,et al.  Query-by-Example Music Information Retrieval by Score-Informed Source Separation and Remixing Technologies , 2011 .

[2]  Daniel P. W. Ellis,et al.  A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures , 2004, Computer Music Journal.

[3]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  Remco C. Veltkamp,et al.  Using transportation distances for measuring melodic similarity , 2003, ISMIR.

[5]  F. L. Hitchcock The Distribution of a Product from Several Sources to Numerous Localities , 1941 .

[6]  Lie Lu,et al.  Music type classification by spectral contrast feature , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[7]  Remco C. Veltkamp,et al.  A Survey of Music Information Retrieval Systems , 2005, ISMIR.

[8]  Beth Logan,et al.  A Content-Based Music Similarity Function , 2001 .

[9]  Nobuaki Ishii,et al.  Fast music information retrieval with indirect matching , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[10]  Marc Leman,et al.  Content-Based Music Information Retrieval: Current Directions and Future Challenges , 2008, Proceedings of the IEEE.

[11]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[12]  Peter Knees,et al.  A survey of music similarity and recommendation from music context data , 2013, ACM Trans. Multim. Comput. Commun. Appl..