Evolving Automatically High-Level Music Descriptors from Acoustic Signals

  • François Pachet
  • Aymeric Zils
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2771)


High-Level music descriptors are key ingredients for music information retrieval systems. Although there is a long tradition in extracting information from acoustic signals, the field of music information extraction is largely heuristic in nature. We present here a heuristic-based generic approach for extracting automatically high-level music descriptors from acoustic signals. This approach is based on Genetic Programming, that is used to build extraction functions as compositions of basic mathematical and signal processing operators. The search is guided by specialized heuristics that embody knowledge about the signal processing functions built by the system. Signal processing patterns are used in order to control the general function extraction methods. Rewriting rules are introduced to simplify overly complex expressions. In addition, a caching system further reduces the computing cost of each cycle. In this paper, we describe the overall system and compare its results against traditional approaches in musical feature extraction à la Mpeg7.


Genetic Programming Recognition Rate Acoustic Signal Audio Signal Colored Noise 
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 2004

Authors and Affiliations

  • François Pachet
    • 1
  • Aymeric Zils
    • 1
  1. 1.Sony CSL ParisParisFrance

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