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Music-inspired texture representation.

Horsburgh, Ben; Craw, Susan; Massie, Stewart


Ben Horsburgh


Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most widely used feature. Current techniques for representing texture however are inspired by speech, not music, therefore music representations are not capturing the correct nature of musical texture. In this paper we investigate two parts of the well-established mel-frequency cepstral coefficients (MFCC) representation: the resolution of mel-frequencies related to the resolution of musical notes; and how best to describe the shape of texture. Through contextualizing these parts, and their relationship to music, a novel music-inspired texture representation is developed. We evaluate this new texture representation by applying it to the task of music recommendation. We use the representation to build three recommendation models, based on current state-of-the-art methods. Our results show that by understanding two key parts of texture representation, it is possible to achieve a significant recommendation improvement. This contribution of a music-inspired texture representation will not only improve content-based representation, but will allow hybrid systems to take advantage of a stronger content component.


HORSBURGH, B., CRAW, S. and MASSIE, S. 2012. Music-inspired texture representation. In Proceedings of the 26th Association for the Advancement of Artificial Intelligence conference on artificial intelligence (AAAI-12), co-located with the 2012 Symposium on educational advances in artificial intelligence (EAAI-12), 22-26 July 2012, Toronto, Canada. Palo Alto: AAAI Press [online], pages 52-58. Available from:

Conference Name 26th Association for the Advancement of Artificial Intelligence conference on artificial intelligence (AAAI-12)
Conference Location Toronto, Canada
Start Date Jul 22, 2012
End Date Jul 26, 2012
Acceptance Date Jul 31, 2012
Online Publication Date Jul 31, 2012
Publication Date Nov 7, 2012
Deposit Date Sep 24, 2013
Publicly Available Date Sep 24, 2013
Publisher Association for the Advancement of Artificial Intelligence
Pages 52-58
ISBN 9781577355687
Public URL
Publisher URL


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