2012 IEEE Region 10 Conference
2012 IEEE Region 10 Conference
The changing music landscape demands new ways of searching, organizing and recommending music to consumers. Content-based music similarity estimation offers a robust solution using a set of audio features. In this paper, we describe the feature extractors to model timbre, rhythm and tempo. We discuss the corresponding feature similarity relations and how the distance measures are combined to quantify music similarity. The proposed system was submitted to 2011 Music Information Retrieval Evaluation eXchange (MIREX) Audio Music Similarity task for validation. Both objective and subjective tests show that the systems achieved an average genre classification of accuracy of 50% across ten genres. Furthermore, the genre classification confusion matrix revealed that the system works best on rap, hiphop and related types of music
de Leon, Franz
49495c02-9bb1-4366-b354-a49268e42c8b
Martinez, Kirk
5f711898-20fc-410e-a007-837d8c57cb18
19 November 2012
de Leon, Franz
49495c02-9bb1-4366-b354-a49268e42c8b
Martinez, Kirk
5f711898-20fc-410e-a007-837d8c57cb18
de Leon, Franz and Martinez, Kirk
(2012)
2012 IEEE Region 10 Conference.
TENCON 2012 - 2012 IEEE Region 10 Conference, Cebu City, Philippines.
19 - 22 Nov 2012.
(doi:10.1109/TENCON.2012.6412211).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The changing music landscape demands new ways of searching, organizing and recommending music to consumers. Content-based music similarity estimation offers a robust solution using a set of audio features. In this paper, we describe the feature extractors to model timbre, rhythm and tempo. We discuss the corresponding feature similarity relations and how the distance measures are combined to quantify music similarity. The proposed system was submitted to 2011 Music Information Retrieval Evaluation eXchange (MIREX) Audio Music Similarity task for validation. Both objective and subjective tests show that the systems achieved an average genre classification of accuracy of 50% across ten genres. Furthermore, the genre classification confusion matrix revealed that the system works best on rap, hiphop and related types of music
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IEEE_TENCON_2012.pdf
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Published date: 19 November 2012
Venue - Dates:
TENCON 2012 - 2012 IEEE Region 10 Conference, Cebu City, Philippines, 2012-11-19 - 2012-11-22
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 361427
URI: http://eprints.soton.ac.uk/id/eprint/361427
PURE UUID: 0a059992-718d-4506-98ff-b3a901a8c4cf
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Date deposited: 24 Jan 2014 08:38
Last modified: 15 Mar 2024 02:53
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Contributors
Author:
Franz de Leon
Author:
Kirk Martinez
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