Article

A singer identification technique for content-based classification of MP3 music objects

Authors:

Chuan-Sung HuangAuthors Info & Claims

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

Pages 438 - 445

https://doi.org/10.1145/584792.584864

Published: 04 November 2002 Publication History

Abstract

As there is a growing amount of MP3 music data available on the Internet today, the problems related to music classification and content-based music retrieval are getting more attention recently. In this paper, we propose an approach to automatically classify MP3 music objects according to their singers. First, the coefficients extracted from the output of the polyphase filters are used to compute the MP3 features for segmentation. Based on these features, an MP3 music object can be decomposed into a sequence of notes (or phonemes). Then for each MP3 phoneme in the training set, its MP3 feature is extracted and used to train an MP3 classifier which can identify the singer of an unknown MP3 music object. Experiments are performed and analyzed to show the effectiveness of the proposed method.

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Zhang LJiang F(2021)Visualizing Symbolic Music via Textualization: An Empirical Study on Chinese Traditional Folk MusicMobile Multimedia Communications10.1007/978-3-030-89814-4_47(647-662)Online publication date: 2-Nov-2021
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Tsai WMa C(2019)Overlapping Singer Identification in Music RecordingsInnovative Solutions and Applications of Web Services Technology10.4018/978-1-5225-7268-8.ch002(23-43)Online publication date: 2019
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Index Terms

A singer identification technique for content-based classification of MP3 music objects
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

November 2002

704 pages

ISBN:1581134924

DOI:10.1145/584792

General Chair:
Charles Nicholas
University of Maryland Baltimore County
,
Program Chairs:
David Grossman
Illinois Institute of Technology
,
Konstantinos Kalpakis
University of Maryland Baltimore County
,
Sajda Qureshi
Erasmus University, Rotterdam
,
Han van Dissel
Erasmus University, Rotterdam
,
Len Seligman
The MITRE Corporation

Copyright © 2002 ACM.

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Published: 04 November 2002

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Cited By

Srinivasa Murthy YKoolagudi SJeshventh Raja T(2021)Singer identification for Indian singers using convolutional neural networksInternational Journal of Speech Technology10.1007/s10772-021-09849-5Online publication date: 4-May-2021
https://doi.org/10.1007/s10772-021-09849-5
Zhang LJiang F(2021)Visualizing Symbolic Music via Textualization: An Empirical Study on Chinese Traditional Folk MusicMobile Multimedia Communications10.1007/978-3-030-89814-4_47(647-662)Online publication date: 2-Nov-2021
https://doi.org/10.1007/978-3-030-89814-4_47
Tsai WMa C(2019)Overlapping Singer Identification in Music RecordingsInnovative Solutions and Applications of Web Services Technology10.4018/978-1-5225-7268-8.ch002(23-43)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7268-8.ch002
Murthy YKoolagudi S(2018)Content-Based Music Information Retrieval (CB-MIR) and Its Applications toward the Music IndustryACM Computing Surveys10.1145/317784951:3(1-46)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3177849
Tsai WMa C(2017)Automatic Identification of Simultaneous and Non-Simultaneous Singers for Music Data IndexingInternational Journal of Web Services Research10.4018/IJWSR.201701010314:1(29-43)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.4018/IJWSR.2017010103
Li TLi L(2011)Music Data MiningMusic Data Mining10.1201/b11041-3(3-42)Online publication date: 28-Jul-2011
https://doi.org/10.1201/b11041-3
Kobayakawa MHoshi M(2010)A method for music structure analysis using MPEG-4 TwinVQ audio compressionProceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I10.5555/1884564.1884575(100-111)Online publication date: 21-Sep-2010
https://dl.acm.org/doi/10.5555/1884564.1884575
Li WLiu YXue XCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)Robust audio identification for MP3 popular musicProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835554(627-634)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835554
Shen JShepherd JCui BTan K(2009)A novel framework for efficient automated singer identification in large music databasesACM Transactions on Information Systems10.1145/1508850.150885627:3(1-31)Online publication date: 19-May-2009
https://dl.acm.org/doi/10.1145/1508850.1508856
Shen JCui BShepherd JTan KEfthimiadis EDumais SHawking DJärvelin, K(2006)Towards efficient automated singer identification in large music databasesProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148184(59-66)Online publication date: 6-Aug-2006
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