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Fuzzy Rule Based System Ensemble for Music Genre Classification

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

Automatic music retrieval processes rely on classification and tagging systems. Among the tags usually employed for classifying music, genre is a prominent one. This paper presents an ensemble of classifiers that uses a hybrid genetic fuzzy approach. By using a set of Fuzzy Rule Based Systems automatically tuned by means of a Genetic Algorithm, and structured in two layers, the system is capable of correctly classifying classical and jazz samples randomly chosen from a wide set of authors and styles.

The ensemble is built on top of a previously developed method that profits from non-precise information by using Fuzzy Systems. The inherently ambiguous information frequently related to music genre is properly managed by a Fuzzy Rule Based System that focuses on random samples extracted from the audio to be analyzed. A set of these Fuzzy Rule Based Systems are then applied simultaneously to a number of samples, and the final system is in charge of processing the partial information obtained by each of the Fuzzy Rule Based System.

The experimental setup and results take into account harmonic principles and their relationship with the specific genre considered. The system is capable of providing good classification accuracy by using an extremely narrow set of features.

Keywords

  • Fuzzy Rule
  • Test Step
  • Classical Music
  • Training Step
  • Music Genre

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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Fernández, F., Chávez, F. (2012). Fuzzy Rule Based System Ensemble for Music Genre Classification. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-29142-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

  • eBook Packages: Computer ScienceComputer Science (R0)