research-article

Music genre classification using explicit semantic analysis

Authors:

Kamelia Aryafar,

Ali ShokoufandehAuthors Info & Claims

MIRUM '11: Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies

Pages 33 - 38

https://doi.org/10.1145/2072529.2072539

Published: 30 November 2011 Publication History

Abstract

Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features.

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Cited By

Murindanyi SHamza KKagumire SMarvin G(2024)Responsible Music Genre Classification Using Interpretable Model-Agnostic Visual ExplainersSN Computer Science10.1007/s42979-024-03584-96:1Online publication date: 27-Dec-2024
https://doi.org/10.1007/s42979-024-03584-9
Singh YBiswas A(2022)Robustness of musical features on deep learning models for music genre classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116879199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116879
Coca Salazar A(2022)Hierarchical mining with complex networks for music genre classificationDigital Signal Processing10.1016/j.dsp.2022.103559127:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.dsp.2022.103559
Show More Cited By

Index Terms

Music genre classification using explicit semantic analysis
1. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Conferences

MIRUM '11: Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies

November 2011

70 pages

ISBN:9781450309868

DOI:10.1145/2072529

General Chairs:
Cynthia Liem
Delft University of Technology, The Netherlands
,
Meinard Müller
Saarland University & MPI Informatik, Germany
,
Douglas Eck
Google Inc., USA
,
George Tzanetakis
University of Victoria, Canada

Copyright © 2011 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 November 2011

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MM '11: ACM Multimedia Conference

November 30, 2011

Arizona, Scottsdale, USA

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Cited By

Murindanyi SHamza KKagumire SMarvin G(2024)Responsible Music Genre Classification Using Interpretable Model-Agnostic Visual ExplainersSN Computer Science10.1007/s42979-024-03584-96:1Online publication date: 27-Dec-2024
https://doi.org/10.1007/s42979-024-03584-9
Singh YBiswas A(2022)Robustness of musical features on deep learning models for music genre classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116879199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116879
Coca Salazar A(2022)Hierarchical mining with complex networks for music genre classificationDigital Signal Processing10.1016/j.dsp.2022.103559127:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.dsp.2022.103559
Medhat FChesmore DRobinson J(2020)Masked Conditional Neural Networks for sound classificationApplied Soft Computing10.1016/j.asoc.2020.106073(106073)Online publication date: Jan-2020
https://doi.org/10.1016/j.asoc.2020.106073
Medhat FChesmore DRobinson J(2017)Music Genre Classification Using Masked Conditional Neural NetworksNeural Information Processing10.1007/978-3-319-70096-0_49(470-481)Online publication date: 26-Oct-2017
https://doi.org/10.1007/978-3-319-70096-0_49
Rajanna AAryafar KShokoufandeh APtucha R(2015)Deep Neural Networks: A Case Study for Music Genre Classification2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2015.160(655-660)Online publication date: Dec-2015
https://doi.org/10.1109/ICMLA.2015.160
Aryafar KShokoufandeh A(2015)Fusion of Text and Audio Semantic Representations Through CCAMultimodal Pattern Recognition of Social Signals in Human-Computer-Interaction10.1007/978-3-319-14899-1_7(66-73)Online publication date: 4-Jan-2015
https://doi.org/10.1007/978-3-319-14899-1_7
Rahutomo FAritsugi M(2014)Econo-ESA in semantic text similaritySpringerPlus10.1186/2193-1801-3-1493:1Online publication date: 19-Mar-2014
https://doi.org/10.1186/2193-1801-3-149
Su LYeh CLiu JWang JYang Y(2014)A Systematic Evaluation of the Bag-of-Frames Representation for Music Information RetrievalIEEE Transactions on Multimedia10.1109/TMM.2014.231101616:5(1188-1200)Online publication date: Aug-2014
https://doi.org/10.1109/TMM.2014.2311016
Panagakis YKotropoulos CArce G(2014)Music genre classification via joint sparse low-rank representation of audio featuresIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2014.235577422:12(1905-1917)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1109/TASLP.2014.2355774
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