A multimodal approach to song-level style identification in pop/rock using similarity metrics

Research output: Contribution to conferencePaper

Abstract

This paper presents a multimodal approach to style identification in pop/rock music. Considering the intuitive feelings of similarity from the listener's perspective, this study focuses on features that are computed using similarity metrics for melodies, harmonies, and audio signals for style identification. Support vector machine is used as a binary classifier to determine if two songs are created by the same artist given their similarity distances in the three aspects. Experiments are conducted using songs of four well-known pop/rock bands from 6 albums. The preliminary result shows that the approach achieves the best result in correct rate of 85% using only seven similarity metrics.

Original languageEnglish (US)
Pages321-324
Number of pages4
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
Event2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States
Duration: Dec 4 2013Dec 7 2013

Other

Other2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
CountryUnited States
CityMiami, FL
Period12/4/1312/7/13

Keywords

  • Gaussian mixture models
  • Style
  • melodic contour
  • music similarity
  • n-grams

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Chuan, C. H. (2013). A multimodal approach to song-level style identification in pop/rock using similarity metrics. 321-324. Paper presented at 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, Miami, FL, United States. https://doi.org/10.1109/ICMLA.2013.143