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  • Research Article
  • Open Access

A Model-Based Approach to Constructing Music Similarity Functions

EURASIP Journal on Advances in Signal Processing20062007:024602

https://doi.org/10.1155/2007/24602

  • Received: 1 December 2005
  • Accepted: 13 August 2006
  • Published:

Abstract

Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.

Keywords

  • Information Technology
  • Euclidean Distance
  • Spectral Feature
  • Feature Space
  • Quantum Information

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Authors’ Affiliations

(1)
School of Computer Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
(2)
Sun Microsystems Laboratories, Sun Microsystems, Inc., Burlington, MA 01803, USA

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