research-article

Improving automatic music genre classification with hybrid content-based feature vectors

Authors:

Carlos N. Silla, Jr.,

Alessandro L. Koerich,

Celso A. A. KaestnerAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 1702 - 1707

https://doi.org/10.1145/1774088.1774453

Published: 22 March 2010 Publication History

Abstract

Current research on the task of automatic music genre classification has been focusing on new classification approaches based on combining information from other sources than the music signal. The reason for this is that the use of content-based approaches, i.e. using features extracted directly from the audio signal, seems to have reached a glass ceiling. In this work we show that by using different types of content-based features together it is possible to substantially improve the classification accuracy. This is an interesting result as different types of content-based features aim, at a conceptual level, to capture the same type of information. In order to identify which types of content-based features are responsible for the predictive accuracy gain, we also used a feature selection (FS) approach based on a genetic algorithm (GA). The analysis of the results in two databases shows that the use of the GA for FS succeeds in selecting a representative subset without significant loss in accuracy. It also shows that all the different types of content-based features employed are important for the improvement of the accuracy in classifying music genres.

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Cited By

Castillo JFlores M(2021)Web-Based Music Genre Classification for Timeline Song Visualization and AnalysisIEEE Access10.1109/ACCESS.2021.30538649(18801-18816)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3053864
Ramírez JFlores M(2019)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-9Online publication date: 27-Nov-2019
https://doi.org/10.1007/s10844-019-00582-9
Muda NMuda AHuoy C(2018)Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre ClassificationHybrid Intelligent Systems10.1007/978-3-319-76351-4_25(242-251)Online publication date: 16-Mar-2018
https://doi.org/10.1007/978-3-319-76351-4_25
Show More Cited By

Index Terms

Improving automatic music genre classification with hybrid content-based feature vectors
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Conferences

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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SAC'10: The 2010 ACM Symposium on Applied Computing

March 22 - 26, 2010

Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Citations

Cited By

Castillo JFlores M(2021)Web-Based Music Genre Classification for Timeline Song Visualization and AnalysisIEEE Access10.1109/ACCESS.2021.30538649(18801-18816)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3053864
Ramírez JFlores M(2019)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-9Online publication date: 27-Nov-2019
https://doi.org/10.1007/s10844-019-00582-9
Muda NMuda AHuoy C(2018)Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre ClassificationHybrid Intelligent Systems10.1007/978-3-319-76351-4_25(242-251)Online publication date: 16-Mar-2018
https://doi.org/10.1007/978-3-319-76351-4_25
Kirmaci BOgul H(2015)Evaluating text features for lyrics-based songwriter prediction2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES)10.1109/INES.2015.7329743(405-409)Online publication date: Sep-2015
https://doi.org/10.1109/INES.2015.7329743
Srinivas MRoy DMohan C(2014)Music genre classification using On-line Dictionary Learning2014 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2014.6889516(1937-1941)Online publication date: Jul-2014
https://doi.org/10.1109/IJCNN.2014.6889516
Sigtia SDixon S(2014)Improved music feature learning with deep neural networks2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2014.6854949(6959-6963)Online publication date: May-2014
https://doi.org/10.1109/ICASSP.2014.6854949
Sturm B(2014)A Survey of Evaluation in Music Genre RecognitionAdaptive Multimedia Retrieval: Semantics, Context, and Adaptation10.1007/978-3-319-12093-5_2(29-66)Online publication date: 29-Oct-2014
https://doi.org/10.1007/978-3-319-12093-5_2
de Lima ANunes RRibeiro RSilla C(2014)Nordic Music Genre Classification Using Song LyricsNatural Language Processing and Information Systems10.1007/978-3-319-07983-7_14(89-100)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07983-7_14
Piccoli HSilla CLéon PPertusa A(2013)An Evaluation of Symbolic Feature Sets and Their Combination for Music Genre ClassificationProceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics10.1109/SMC.2013.327(1901-1905)Online publication date: 13-Oct-2013
https://dl.acm.org/doi/10.1109/SMC.2013.327
Draman NAhmad SMuda A(2011)Recognizing Patterns of Music Signals to Songs Classification Using Modified AIS-Based ClassifierSoftware Engineering and Computer Systems10.1007/978-3-642-22191-0_64(724-737)Online publication date: 2011
https://doi.org/10.1007/978-3-642-22191-0_64

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