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Improving music genre classification using collaborative tagging data

Published: 09 February 2009 Publication History

Abstract

As a fundamental and critical component of music information retrieval (MIR) systems, music genre classification has attracted considerable research attention. Automatically classifying music by genre is, however, a challenging problem due to the fact that music is an evolving art. While most of the existing work categorizes music using features extracted from music audio signals, in this paper, we propose to exploit the semantic information embedded in tags supplied by users of social networking websites. Particularly, we consider the tag information by creating a graph of tracks so that tracks are neighbors if they are similar in terms of their associated tags. Two classification methods based on the track graph are developed. The first one employs a classification scheme which simultaneously considers the audio content and neighborhood of tracks. In contrast, the second one is a two-level classifier which initializes genre label for unknown tracks using their audio content, and then iteratively updates the genres considering the influence from their neighbors. A set of optimizing strategies are designed for the purpose of further enhancing the quality of the two-level classifier. Extensive experiments are conducted on real-world data collected from Last.fm. Promising experimental results demonstrate the benefit of using tags for accurate music genre classification.

References

[1]
R. Angelova and G. Weikum. Graph-based text classification: learn from your neighbors. In SIGIR, pages 485--492, 2006.
[2]
J.-J. Aucouturier and F. Pachet. Representing musical genre: A state of the art. Journal of New Music Research., 32(1):83--93, 2003.
[3]
S. Bao, G.-R. Xue, X. Wu, Y. Yu, B. Fei, and Z. Su. Optimizing web search using social annotations. In WWW, pages 501--510, 2007.
[4]
B. Berendt and C. Hanser. Tags are not metadata, but just more content - to some people. In ICWSM, 2007.
[5]
C. H. Brooks and N. Montanez. Improved annotation of the blogosphere via autotagging and hierarchical clustering. In WWW, pages 625--632, 2006.
[6]
J. J. Burred and A. Lerch. A hierarchical approach to automatic musical genre classification. In 6th Int. Conf. Digital Audio Effects, 2003.
[7]
S. Chakrabarti, B. Dom, and P. Indyk. Enhanced hypertext categorization using hyperlinks. In SIGMOD Conference, pages 307--318, 1998.
[8]
B. Cui, H. V. Jagadish, B. C. Ooi, and K.-L. Tan. Compacting music signatures for efficient music retrieval. In EDBT, pages 229--240, 2008.
[9]
R. Dannenberg, J. Foote, G. Tzanetakis, and C. Weare. Panel: new directions in music information retrieval. In Proc. Int. Computer Music Conf., 2001.
[10]
P. A. Dmitriev, N. Eiron, M. Fontoura, and E. J. Shekita. Using annotations in enterprise search. In WWW, pages 811--817, 2006.
[11]
M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. In WWW, pages 193--202, 2006.
[12]
S. Golder and B. A. Huberman. The structure of collaborative tagging systems. In http://www.hpl.hp.com/research/idl/papers/tags/tags.pdf, 2006.
[13]
F. Gouyon, S. Dixon, E. Pampalk, and G. Widmer. Evaluating rhythmic descriptors for musical genre classification. In AES 25th Int. Conf., 2004.
[14]
H. Halpin, V. Robu, and H. Shepherd. The complex dynamics of collaborative tagging. In WWW, pages 211--220, 2007.
[15]
T. Li, M. Ogihara, and Q. Li. A comparative study on content-based music genre classification. In SIGIR, pages 282--289, 2003.
[16]
Q. Lu and L. Getoor. Link-based classification. In ICML, pages 496--503, 2003.
[17]
S. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study. volume 8, pages 935--983, 2007.
[18]
M. Mandel and D. Ellis. Song-level features and support vector machines for music classification. In ISMIR, pages 594--599, 2005.
[19]
C. McKay and I. Fujinaga. Automatic genre classification using large high-level musical feature sets. In ISMIR, 2004.
[20]
C. McKay and I. Fujinaga. Musical genre classification: Is it worth pursuing and how can it be improved? In ISMIR, pages 101--106, 2006.
[21]
E. Pampalk, A. Flexer, and G. Widmer. Improvements of audio-based music similarity and genre classificaton. In ISMIR, pages 628--633, 2005.
[22]
L. Pelkowitz. A continuous relaxation labelling algorithm for markov random fields. volume 20, pages 709--715, 1990.
[23]
N. Scaringella, G. Zoia, and D. Mlynek. Automatic genre classification of music content: a survey. IEEE Singal Processing Magazine., 23(2):133--141, 2006.
[24]
P. Schmitz. Inducing ontology from flickr tags. In the workshop on Collaborative Web Tagging at WWW, 2006.
[25]
B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In UAI, pages 485--492, 2002.
[26]
G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Trans. on Speech and Audio Processing, 10(5):293--302, July 2002.
[27]
K. West and S. Cox. Finding an optimal segmentation for audio genre classification. In ISMIR, pages 680--685, 2005.
[28]
B. Whitman and P. Smaragdis. Combining musical and cultural features for intelligent style detection. In ISMIR, 2002.
[29]
X. Wu, L. Zhang, and Y. Yu. Exploring social annotations for the semantic web. In WWW, pages 417--426, 2006.

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        cover image ACM Conferences
        WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining
        February 2009
        314 pages
        ISBN:9781605583907
        DOI:10.1145/1498759
        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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        Published: 09 February 2009

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        Author Tags

        1. exploiting tag semantics
        2. music genre classification
        3. relaxation labeling

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        • (2018)Improvised emotion and genre detection for songs through signal processing and genetic algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.506531:14Online publication date: 3-Dec-2018
        • (2017)VOCALOID Creator Search Using Music Genre and ImpressionTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.WII-K32:1(WII-K_1-12)Online publication date: 2017
        • (2016)Making Music TogetherProceedings of the Audio Mostly 201610.1145/2986416.2986432(186-193)Online publication date: 4-Oct-2016
        • (2016)A survey of tag-based information retrievalInternational Journal of Multimedia Information Retrieval10.1007/s13735-016-0115-66:2(99-113)Online publication date: 9-Dec-2016
        • (2016)Searching for musicPersonal and Ubiquitous Computing10.1007/s00779-016-0911-220:4(559-571)Online publication date: 1-Aug-2016
        • (2015)Distributed music classification using Random Vector Functional-Link nets2015 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2015.7280333(1-8)Online publication date: Jul-2015
        • (2015)Designing a graph-based framework to support a multi-modal approach for music information retrievalMultimedia Tools and Applications10.1007/s11042-014-1860-274:15(5401-5427)Online publication date: 1-Jul-2015
        • (2014)DenGraph-HOExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1204631:5(469-479)Online publication date: 1-Nov-2014
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