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Volumn , Issue , 2010, Pages 507-512

Scalable genre and tag prediction with spectral covariance

Author keywords

[No Author keywords available]

Indexed keywords

CEPSTRAL ANALYSIS; CEPSTRAL FEATURES; COMPETITIVE PERFORMANCE; LINEAR CLASSIFIERS; SOCIAL TAGS; SPECTRAL FEATURE; STATE-OF-THE-ART PERFORMANCE;

EID: 84873599467     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (21)

References (14)
  • 2
    • 57049179026 scopus 로고    scopus 로고
    • Autotagger: A model for predicting social tags from acoustic features on large music databases
    • T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere. Autotagger: A model for predicting social tags from acoustic features on large music databases. Journal of New Music Research, 37(2):115-135, 2008.
    • (2008) Journal of New Music Research , vol.37 , Issue.2 , pp. 115-135
    • Bertin-Mahieux, T.1    Eck, D.2    Maillet, F.3    Lamere, P.4
  • 3
    • 84873657155 scopus 로고    scopus 로고
    • A music classification method based on timbral features
    • Kobe, Japan, October
    • Thibault Langlois and Gonalo Marques. A music classification method based on timbral features. In ISMIR, Kobe, Japan, October 2009.
    • (2009) ISMIR
    • Langlois, T.1    Marques, G.2
  • 4
    • 84892457941 scopus 로고    scopus 로고
    • Input-agreement: A new mechanism for data collection using human computation games
    • Edith L. M. Law and Luis von Ahn. Input-agreement: A new mechanism for data collection using human computation games. In CHI, pages 1197-1206, 2009.
    • (2009) CHI , pp. 1197-1206
    • Law, E.L.M.1    Von Ahn, L.2
  • 6
    • 0009985115 scopus 로고    scopus 로고
    • Mel frequency cepstral coefficients for music modeling
    • Plymouth, Mass. October
    • Beth Logan. Mel frequency cepstral coefficients for music modeling. In ISMIR, Plymouth, Mass., October 2000.
    • (2000) ISMIR
    • Logan, B.1
  • 7
    • 57049157739 scopus 로고    scopus 로고
    • A web-based game for collecting music metadata
    • M. Mandel and D. Ellis. A web-based game for collecting music metadata. J. New Music Research, 37(2):151-165, 2008.
    • (2008) J. New Music Research , vol.37 , Issue.2 , pp. 151-165
    • Mandel, M.1    Ellis, D.2
  • 8
    • 84873528643 scopus 로고    scopus 로고
    • Song-level features and support vector machines for music classification
    • London, UK, September
    • Michael I. Mandel and Daniel P.W. Ellis. Song-level features and support vector machines for music classification. In ISMIR, pages 594-599, London, UK, September 2005.
    • (2005) ISMIR , pp. 594-599
    • Mandel, M.I.1    Ellis, D.P.W.2
  • 9
    • 0038133939 scopus 로고
    • Distance measures for speech recognition, psychological and instrumental
    • P. Mermelstein. Distance measures for speech recognition, psychological and instrumental. Pattern Recognition and Artificial Intelligence, pages 374-388, 1976.
    • (1976) Pattern Recognition and Artificial Intelligence , pp. 374-388
    • Mermelstein, P.1
  • 10
    • 84873668627 scopus 로고    scopus 로고
    • Music genre classification using locality preserving non-negative tensor factorization and sparse representations
    • Kobe, Japan, October
    • Yannis Panagakis, Constantine Kotropoulos, and Gonzalo R. Arce. Music genre classification using locality preserving non-negative tensor factorization and sparse representations. In ISMIR, Kobe, Japan, October 2009.
    • (2009) ISMIR
    • Panagakis, Y.1    Kotropoulos, C.2    Arce, G.R.3
  • 12
    • 0010053023 scopus 로고    scopus 로고
    • Automatic musical genre classification of audio, signals
    • Bloomington, Indiana, October
    • G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In ISMIR, Bloomington, Indiana, October 2001.
    • (2001) ISMIR
    • Tzanetakis, G.1    Essl, G.2    Cook, P.3
  • 14
    • 84873531705 scopus 로고    scopus 로고
    • Finding an optimal segmentation for audio genre classification
    • London, UK, October
    • Kris West and Stephen Cox. Finding an optimal segmentation for audio genre classification. In ISMIR, London, UK, October 2005.
    • (2005) ISMIR
    • West, K.1    Cox, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.