Emotion recognition from music enhanced by domain knowledge

Shu, Y; Xu, G

Emotion recognition from music enhanced by domain knowledge

Shu, Y

Xu, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11670 LNAI pp. 121 - 134
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Shu, Y https://orcid.org/0000-0001-6319-6037	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.available	2019-06-03	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11670 LNAI pp. 121 - 134	en_US
dc.identifier.isbn	9783030299071	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133790
dc.description.abstract	© Springer Nature Switzerland AG 2019. Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies between different main musical elements and emotions from the summarized music theory. Then, we transfer the domain knowledge to constraints, and formulate affective music analysis as a constrained optimization problem. Experimental results on the Music in 2015 database and the AMG1608 database demonstrate that the proposed music content analyses method outperforms the state-of-the-art performance prediction methods.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-29908-8_10	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Emotion recognition from music enhanced by domain knowledge	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11670 LNAI	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11670 LNAI	en_US

Abstract:

© Springer Nature Switzerland AG 2019. Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies between different main musical elements and emotions from the summarized music theory. Then, we transfer the domain knowledge to constraints, and formulate affective music analysis as a constrained optimization problem. Experimental results on the Music in 2015 database and the AMG1608 database demonstrate that the proposed music content analyses method outperforms the state-of-the-art performance prediction methods.

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http://hdl.handle.net/10453/133790