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An Audio-Visual Approach to Music Genre Classification through Affective Color Features

  • Alexander Schindler
  • Andreas Rauber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

This paper presents a study on classifying music by affective visual information extracted frommusic videos. The proposed audio-visual approach analyzes genre specific utilization of color. A comprehensive set of color specific image processing features used for affect and emotion recognition derived from psychological experiments or art-theory is evaluated in the visual and multi-modal domain against contemporary audio content descriptors. The evaluation of the presented color features is based on comparative classification experiments on the newly introduced ‘Music Video Dataset’. Results show that a combination of the modalities can improve non-timbral and rhythmic features but show insignificant effects on high performing audio features.

Keywords

Support Vector Machine Random Forest Image Retrieval Music Video Audio Feature 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Schindler
    • 1
    • 2
  • Andreas Rauber
    • 1
  1. 1.Department of Software Technology and Interactive SystemsVienna University of TechnologyAustria
  2. 2.Information management, Digital Safety and Security DepartmentAIT Austrian Institute of TechnologyAustria

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