메뉴 건너뛰기




Volumn , Issue , 2012, Pages 89-98

The acoustic emotion gaussians model for emotion-based music annotation and retrieval

Author keywords

automatic music emotion recognition; computational emotion model; em algorithm; gaussian mixture model; music retrieval

Indexed keywords

EM ALGORITHMS; EMOTION MODELS; EMOTION RECOGNITION; GAUSSIAN MIXTURE MODEL; MUSIC RETRIEVAL;

EID: 84871358453     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2393347.2393367     Document Type: Conference Paper
Times cited : (46)

References (31)
  • 2
    • 79953822842 scopus 로고    scopus 로고
    • Affect detection: An interdisciplinary review of models, methods, and their applications
    • R. A. Calvo and S. D'Mello. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans. Affective Computing, 1(1):18-37, 2010.
    • (2010) IEEE Trans. Affective Computing , vol.1 , Issue.1 , pp. 18-37
    • Calvo, R.A.1    D'Mello, S.2
  • 3
    • 78049403723 scopus 로고    scopus 로고
    • Differential entropic clustering of multivariate Gaussians
    • J. V. Davis and I. S. Dhillon. Differential entropic clustering of multivariate Gaussians. In NIPS, 2006.
    • (2006) NIPS
    • Davis, J.V.1    Dhillon, I.S.2
  • 4
    • 84873646150 scopus 로고    scopus 로고
    • Prediction of multidimensional emotional ratings in music from audio using multivariate regression models
    • T. Eerola, O. Lartillot, and P. Toiviainen. Prediction of multidimensional emotional ratings in music from audio using multivariate regression models. In ISMIR, 2009.
    • (2009) ISMIR
    • Eerola, T.1    Lartillot, O.2    Toiviainen, P.3
  • 5
    • 33745759392 scopus 로고    scopus 로고
    • Emotion perceived and emotion felt: Same or different?
    • A. Gabrielsson. Emotion perceived and emotion felt: Same or different? Musicae Scientiae, pages 123-147, 2002.
    • (2002) Musicae Scientiae , pp. 123-147
    • Gabrielsson, A.1
  • 6
    • 34249013658 scopus 로고    scopus 로고
    • Relationships between musical structure and psychophysiological measures of emotion
    • P. Gomez and B. Danuser. Relationships between musical structure and psychophysiological measures of emotion. Emotion, 7(2):377-87, 2007.
    • (2007) Emotion , vol.7 , Issue.2 , pp. 377-387
    • Gomez, P.1    Danuser, B.2
  • 8
    • 34547516258 scopus 로고    scopus 로고
    • Approximating the Kullback Leibler divergence between Gaussian mixture models
    • J. Hershey and P. Olsen. Approximating the Kullback Leibler divergence between Gaussian mixture models. In ICASSP, pages 317-320, 2007.
    • (2007) ICASSP , pp. 317-320
    • Hershey, J.1    Olsen, P.2
  • 9
    • 84873433681 scopus 로고    scopus 로고
    • The 2007 MIREX audio mood classiffication task: Lessons learned
    • X. Hu, J. S. Downie, C. Laurier, M. Bay, and A. F. Ehmann. The 2007 MIREX audio mood classiffication task: Lessons learned. In ISMIR, 2008.
    • (2008) ISMIR
    • Hu, X.1    Downie, J.S.2    Laurier, C.3    Bay, M.4    Ehmann, A.F.5
  • 10
    • 1842637192 scopus 로고    scopus 로고
    • Cumulated gain-based evaluation of IR techniques
    • K. Jarvelin and J. Kekalainen. Cumulated gain-based evaluation of IR techniques. ACM Trans. on Info. Syst., 20(4):422-446, 2002.
    • (2002) ACM Trans. on Info. Syst. , vol.20 , Issue.4 , pp. 422-446
    • Jarvelin, K.1    Kekalainen, J.2
  • 11
    • 85035841760 scopus 로고    scopus 로고
    • Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening
    • P. N. Juslin and P. Laukka. Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. J. New Music Res., 33(3):217-238, 2004.
    • (2004) J. New Music Res. , vol.33 , Issue.3 , pp. 217-238
    • Juslin, P.N.1    Laukka, P.2
  • 12
    • 84873591302 scopus 로고    scopus 로고
    • Music emotion recognition: A state of the art review
    • Y. E. Kim and et al. Music emotion recognition: A state of the art review. In ISMIR, 2010.
    • (2010) ISMIR
    • Kim, Y.E.1
  • 14
    • 84872726756 scopus 로고    scopus 로고
    • A Matlab toolbox for musical feature extraction from audio
    • O. Lartillot and P. Toiviainen. A Matlab toolbox for musical feature extraction from audio. In DAFx, 2007.
    • (2007) DAFx
    • Lartillot, O.1    Toiviainen, P.2
  • 15
    • 33744975700 scopus 로고    scopus 로고
    • Automatic mood detection and tracking of music audio signals
    • L. Lu, D. Liu, and H. Zhang. Automatic mood detection and tracking of music audio signals. IEEE Trans. Audio, Speech and Lang. Proc., 14(1):5-18, 2006.
    • (2006) IEEE Trans. Audio, Speech and Lang. Proc. , vol.14 , Issue.1 , pp. 5-18
    • Lu, L.1    Liu, D.2    Zhang, H.3
  • 16
    • 49549124035 scopus 로고    scopus 로고
    • Automatic emotion prediction of song excerpts: Index construction, algorithm design, and empirical comparison
    • K. F. MacDorman, S. Ough, and C.-C. Ho. Automatic emotion prediction of song excerpts: Index construction, algorithm design, and empirical comparison. J. New Music Res., 36(4):281-299, 2007.
    • (2007) J. New Music Res. , vol.36 , Issue.4 , pp. 281-299
    • MacDorman, K.F.1    Ough, S.2    Ho, C.-C.3
  • 19
    • 84555174290 scopus 로고    scopus 로고
    • Prediction of time-varying musical mood distributions from audio
    • E. M. Schmidt and Y. E. Kim. Prediction of time-varying musical mood distributions from audio. In ISMIR, 2010.
    • (2010) ISMIR
    • Schmidt, E.M.1    Kim, Y.E.2
  • 20
    • 79952403274 scopus 로고    scopus 로고
    • Prediction of time-varying musical mood distributions using Kalman filtering
    • E. M. Schmidt and Y. E. Kim. Prediction of time-varying musical mood distributions using Kalman filtering. In ICMLA, 2010.
    • (2010) ICMLA
    • Schmidt, E.M.1    Kim, Y.E.2
  • 21
    • 84872700353 scopus 로고    scopus 로고
    • Modeling musical emotion dynamics with conditional random fields
    • E. M. Schmidt and Y. E. Kim. Modeling musical emotion dynamics with conditional random fields. In ISMIR, 2011.
    • (2011) ISMIR
    • Schmidt, E.M.1    Kim, Y.E.2
  • 23
    • 31144442341 scopus 로고    scopus 로고
    • Modeling perceived emotion with continuous musical features
    • E. Schubert. Modeling perceived emotion with continuous musical features. Music Perception, 21(4):561-585, 2004.
    • (2004) Music Perception , vol.21 , Issue.4 , pp. 561-585
    • Schubert, E.1
  • 24
    • 77952100523 scopus 로고    scopus 로고
    • Cheer Me Up!": Musical and textual features for automatic mood classification
    • B. Schuller, C. Hage, D. Schuller, and G. Rigoll. \Mister D.J., Cheer Me Up!": Musical and textual features for automatic mood classification. J. New Music Res., 39(1):13-34, 2010.
    • (2010) J. New Music Res. , vol.39 , Issue.1 , pp. 13-34
    • Schuller, B.1    Hage, C.2    Schuller, D.3    Rigoll, G.4    Mister, D.J.5
  • 26
    • 84873597750 scopus 로고    scopus 로고
    • A comparative study of collaborative vs. traditional musical mood annotation
    • J. A. Speck, E. M. Schmidt, B. G. Morton, and Y. E. Kim. A comparative study of collaborative vs. traditional musical mood annotation. In ISMIR, 2011.
    • (2011) ISMIR
    • Speck, J.A.1    Schmidt, E.M.2    Morton, B.G.3    Kim, Y.E.4
  • 28
    • 84873594763 scopus 로고    scopus 로고
    • Learning the similarity of audio music in bag-of-frames representation from tagged music data
    • J.-C. Wang, H.-S. Lee, H.-M. Wang, and S.-K. Jeng. Learning the similarity of audio music in bag-of-frames representation from tagged music data. In ISMIR, 2011.
    • (2011) ISMIR
    • Wang, J.-C.1    Lee, H.-S.2    Wang, H.-M.3    Jeng, S.-K.4
  • 30
    • 85008559668 scopus 로고    scopus 로고
    • Predicting the distribution of perceived emotions of a music signal for content retrieval
    • Y.-H. Yang and H. H. Chen. Predicting the distribution of perceived emotions of a music signal for content retrieval. IEEE Trans. Audio, Speech and Lang. Proc., 19(7):2184-2196, 2011.
    • (2011) IEEE Trans. Audio, Speech and Lang. Proc. , vol.19 , Issue.7 , pp. 2184-2196
    • Yang, Y.-H.1    Chen, H.H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.