publication . Preprint . Part of book or chapter of book . 2015

Affective Music Information Retrieval

Ju-Chiang Wang; Yi-Hsuan Yang; Hsin-Min Wang;
Open Access English
  • Published: 18 Feb 2015
Abstract
Comment: 40 pages, 18 figures, 5 tables, author version
Subjects
free text keywords: Computer Science - Information Retrieval, H.3.3, H.5.5
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Download fromView all 2 versions
http://arxiv.org/pdf/1502.0513...
Part of book or chapter of book
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Part of book or chapter of book . 2016
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56 references, page 1 of 4

[1] M. Barthet, G. Fazekas, and M. Sandler. Multidisciplinary perspectives on music emotion recognition: Implications for content and contextbased models. In Proc. Int. Symp. Computer Music Modeling and Retrieval, pages 492{507, 2012.

[2] E. Bigand, S. Vieillard, F. Madurell, J. Marozeau, and A. Dacquet. Multidimensional scaling of emotional responses to music: The e ect of musical expertise and of the duration of the excerpts. Cognition and Emotion, 19(8):1113{1139, 2005. [OpenAIRE]

[3] C. M. Bishop. Pattern Recognition and Machine Learning. SpringerVerlag New York, Inc., 2006.

[4] L. Bottou. Online algorithms and stochastic approximations. In D. Saad, editor, Online Learning and Neural Networks. Cambridge University Press, Cambridge, UK, 1998.

[5] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. Intelligent System and Technology, 2(3):27:1{ 27:39, 2011.

[6] Y.-A. Chen, J.-C. Wang, Y.-H. Yang, and H.-H. Chen. Linear regressionbased adaptation of music emotion recognition models for personalization. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pages 2149{2153, 2014.

[7] Y.-A. Chen, J.-C. Wang, Y.-H. Yang, and H. H. Chen. Personalization of music emotion recognition by mixture model adaptation. IEEE Trans. Audio, Speech, and Language Processing, 2015. Submitted.

[8] Y.-A. Chen, Y.-H. Yang, J.-C. Wang, and H. H. Chen. The AMG1608 dataset for music emotion recognition. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2015. [Online] http://amg1608. blogspot.tw/.

[9] W. Chou. Minimum classi cation error approach in pattern recognition. In W. Chou and B.-H. Juang, editors, Pattern Recognition in Speech and Language Processing. CRC Press, 2003.

[10] G. Collier. Beyond valence and activity in the emotional connotations of music. Psychology of Music, 35(1):110{131, 2007.

[11] J. V. Davis and I. S. Dhillon. Di erential entropic clustering of multivariate Gaussians. In Advances in Neural Information Processing Systems, volume 19, pages 337{344, 2007.

[13] T. Eerola. Modelling emotions in music: Advances in conceptual, contextual and validity issues. In Proc. AES Int. Conf., 2014. [OpenAIRE]

[14] A. Gabrielsson. Emotion perceived and emotion felt: Same or di erent? Musicae Scientiae, pages 123{147, 2002. [OpenAIRE]

[19] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classi cation: A feature analysis. In Proc. Int. Soc. Music Information Retrieval Conf., pages 619{624, 2010.

[20] X. Hu, J. S. Downie, C. Laurier, M. Bay, and A. F. Ehmann. The 2007 MIREX audio mood classi cation task: Lessons learned. In Proc. Int. Soc. Music Information Retrieval Conf., pages 462{467, 2008. [OpenAIRE]

56 references, page 1 of 4
Related research
Abstract
Comment: 40 pages, 18 figures, 5 tables, author version
Subjects
free text keywords: Computer Science - Information Retrieval, H.3.3, H.5.5
Related Organizations
Download fromView all 2 versions
http://arxiv.org/pdf/1502.0513...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book . 2016
Provider: Crossref
56 references, page 1 of 4

[1] M. Barthet, G. Fazekas, and M. Sandler. Multidisciplinary perspectives on music emotion recognition: Implications for content and contextbased models. In Proc. Int. Symp. Computer Music Modeling and Retrieval, pages 492{507, 2012.

[2] E. Bigand, S. Vieillard, F. Madurell, J. Marozeau, and A. Dacquet. Multidimensional scaling of emotional responses to music: The e ect of musical expertise and of the duration of the excerpts. Cognition and Emotion, 19(8):1113{1139, 2005. [OpenAIRE]

[3] C. M. Bishop. Pattern Recognition and Machine Learning. SpringerVerlag New York, Inc., 2006.

[4] L. Bottou. Online algorithms and stochastic approximations. In D. Saad, editor, Online Learning and Neural Networks. Cambridge University Press, Cambridge, UK, 1998.

[5] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. Intelligent System and Technology, 2(3):27:1{ 27:39, 2011.

[6] Y.-A. Chen, J.-C. Wang, Y.-H. Yang, and H.-H. Chen. Linear regressionbased adaptation of music emotion recognition models for personalization. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pages 2149{2153, 2014.

[7] Y.-A. Chen, J.-C. Wang, Y.-H. Yang, and H. H. Chen. Personalization of music emotion recognition by mixture model adaptation. IEEE Trans. Audio, Speech, and Language Processing, 2015. Submitted.

[8] Y.-A. Chen, Y.-H. Yang, J.-C. Wang, and H. H. Chen. The AMG1608 dataset for music emotion recognition. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2015. [Online] http://amg1608. blogspot.tw/.

[9] W. Chou. Minimum classi cation error approach in pattern recognition. In W. Chou and B.-H. Juang, editors, Pattern Recognition in Speech and Language Processing. CRC Press, 2003.

[10] G. Collier. Beyond valence and activity in the emotional connotations of music. Psychology of Music, 35(1):110{131, 2007.

[11] J. V. Davis and I. S. Dhillon. Di erential entropic clustering of multivariate Gaussians. In Advances in Neural Information Processing Systems, volume 19, pages 337{344, 2007.

[13] T. Eerola. Modelling emotions in music: Advances in conceptual, contextual and validity issues. In Proc. AES Int. Conf., 2014. [OpenAIRE]

[14] A. Gabrielsson. Emotion perceived and emotion felt: Same or di erent? Musicae Scientiae, pages 123{147, 2002. [OpenAIRE]

[19] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classi cation: A feature analysis. In Proc. Int. Soc. Music Information Retrieval Conf., pages 619{624, 2010.

[20] X. Hu, J. S. Downie, C. Laurier, M. Bay, and A. F. Ehmann. The 2007 MIREX audio mood classi cation task: Lessons learned. In Proc. Int. Soc. Music Information Retrieval Conf., pages 462{467, 2008. [OpenAIRE]

56 references, page 1 of 4
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