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Evaluation of Modeling Music Similarity Perception Via Feature Subset Selection

  • D. N. Sotiropoulos
  • A. S. Lampropoulos
  • G. A. Tsihrintzis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

In this paper, we describe and discuss the evaluation process and results of a content-based music retrieval system that we have developed. In our system, user models embody the ability of evolving and using different music similarity measures for different users. Specifically, a user-supplied relevance feedback and related neural network-based incremental learning procedures allows our system to determine which subset of a set of objective acoustic features approximates more efficiently the subjective music similarity perception of an individual user. The evaluation results verify our hypothesis of a direct relation between subjective music similarity perception and objective acoustic feature subsets. Moreover, it is shown that, after training, retrieved music pieces exhibit significantly improved perceived similarity to user-targeted music pieces.

Keywords

Feature Subset Relevance Feedback Incremental Learning Music Piece Feature Subset Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • D. N. Sotiropoulos
    • 1
  • A. S. Lampropoulos
    • 1
  • G. A. Tsihrintzis
    • 1
  1. 1.University of Piraeus, Department of Informatics, 80 Karaoli and Dimitriou St, Piraeus 18534Greece

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