Enhancing music information retrieval by incorporating image-based local features

Leszek Kaliciak*, Ben Horsburgh, Dawei Song, Nirmalie Wiratunga, Jeff Pan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)


This paper presents a novel approach to music genre classification. Having represented music tracks in the form of two dimensional images, we apply the "bag of visual words" method from visual IR in order to classify the songs into 19 genres. By switching to visual domain, we can abstract from musical concepts such as melody, timbre and rhythm. We obtained classification accuracy of 46% (with 5% theoretical baseline for random classification) which is comparable with existing state-of-the-art approaches. Moreover, the novel features characterize different properties of the signal than standard methods. Therefore, the combination of them should further improve the performance of existing techniques. The motivation behind this work was the hypothesis, that 2D images of music tracs (spectrograms) perceived as similar would correspond to the same music genres. Conversely, it is possible to treat real life images as spectrograms and utilize music-based features to represent these images in a vector form. This points to an interesting interchangeability between visual and music information retrieval.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
Number of pages12
Publication statusPublished - 2012
Event8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China
Duration: 17 Dec 201219 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th Asia Information Retrieval Societies Conference, AIRS 2012


  • Co-occurrence matrix
  • Colour moments
  • Fourier transform
  • K-means algorithm
  • Local features


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