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Volumn , Issue , 2009, Pages 657-662

Music mood and theme classification - A hybrid approach

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO FEATURES; CLASSIFICATION ACCURACY; CLASSIFICATION TASKS; COLLABORATIVE USERS; GROUND TRUTH; HYBRID APPROACH; INFORMATION NEED; INFORMATION SEEKING; LAST.FM; MUSIC PERCEPTION; MUSIC RETRIEVAL; MUSICAL GENRE; PERCEPTUAL DIMENSIONS; SOCIAL DATUM;

EID: 84862927717     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (67)

References (19)
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  • 3
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    • Automatic mood detection from acoustic music data
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    • (2003) ISMIR
    • Liu, D.1    Lu, L.2    Zhang, H.-J.3
  • 4
    • 1542317649 scopus 로고    scopus 로고
    • Popular music retrieval by detecting mood
    • Y. Feng, Y. Zhuang, and Y. Pan: "Popular music retrieval by detecting mood, " SIGIR, 2003.
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    • Feng, Y.1    Zhuang, Y.2    Pan, Y.3
  • 5
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    • A demonstrator for automatic music mood estimation
    • J. Skowronek, M. McKinney, and S. van de Par: "A demonstrator for automatic music mood estimation, " ISMIR, 2007.
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  • 9
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  • 10
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    • Autotagger: A model for predicting social tags from acoustic features on large music databases
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  • 12
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    • Automatic generation of social tags for music recommendation
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  • 13
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  • 14
    • 70349129938 scopus 로고    scopus 로고
    • Improving music genre classification using collaborative tagging data
    • L. Chen, P. Wright, and W. Nejdl: "Improving music genre classification using collaborative tagging data, " WSDM, pp. 84-93, 2009.
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  • 15
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  • 17
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    • Wu, T.-F.1    Lin, C.-J.2    Weng, R.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.