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A Classification Framework for Similar Music Search

  • Jing Zeng
  • Zhenying He
  • Wei Wang
  • Hai Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7419)

Abstract

This paper has concentrated on how to retrieve a list of songs from music database similar to the specific one. Content-based retrieval of music is one of the most popular research subjects, which mostly focuses on querying the exactly one from database by humming a tune or submitting a recording of music. However, getting some songs similar to, but not exactly the given one could be also interested by people. In this paper, we propose a classification framework to solve this problem using string-based methods. Introducing string-based similarity measure, our framework has lower computational complexity and better effect. We also developed a new distributed clustering algorithm under MapReduce framework, which performed well for massive audio data. Experiments are performed and analyzed to show the efficiency and the effectiveness of our proposed framework.

Keywords

Feature Vector Feature Extraction Method Inverted Index Classification Framework Music Information Retrieval 
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 2012

Authors and Affiliations

  • Jing Zeng
    • 1
  • Zhenying He
    • 1
  • Wei Wang
    • 1
  • Hai Huang
    • 2
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Cultural Institutions Security Branch of Shanghai Public Security BureaShanghaiChina

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