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Design Science Journal
Papers

Music Style Analysis Using the Random Forest Algorithm

Music Style Analysis Using the Random Forest Algorithm

Year: 2012

Section: Design Creativity Methods and Integration

Editor: Duffy, A.; Nagai, Y.; Taura, T.

Author: A. Gómez de Silva Garza; E. Herrera González

Section: Design Creativity Methods and Integration

Pages: 343-350

Abstract

This paper aims to discuss a method for autonomously analyzing a musical style based on the random forest learning algorithm. This algorithm needs to be shown both positive and negative examples of the concept one is trying to teach it. The algorithm uses the Hidden Markov Model (HMM) of each positive and negative piece of music to learn to distinguish the desired musical style from melodies that don‘t belong to it. The HMM is acquired from the coefficients that are generated by the Wavelet Transform of each piece of music. The output of the random forest algorithm codifies the solution space describing the desired style in an abstract and compact manner. This information can later be used for recognizing and/or generating melodies that fit within the analyzed style, a capability that can be of much use in computational models of design and computational creativity.

Keywords: style analysis, music style, markov model, wavelets, random forest

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ds conferenceICDC 2012: The 2nd International Conference on Design Creativity

ICDC 2012: The 2nd International Conference on Design CreativityDesign Creativity is an important and interesting topic of study in design. Since it involves the profound and essential nature of design, design creativity is expected to be a key in not only addressing the social problems that we are facing, but also producing an innate appreciation for beauty and happiness in our minds. In order to elucidate the nature of design creativity, the following issues are being studied: - Cognitive processes underlying design creativity - Computational models of design...

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