Music retrieval in joint emotion space using audio features and emotional tags

James J. Deng; C. H.C. Leung

doi:10.1007/978-3-642-35725-1_48

Music retrieval in joint emotion space using audio features and emotional tags

James J. Deng
, C. H.C. Leung

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

7 Citations (Scopus)

Abstract

Emotion-based music retrieval provides a natural and humanized way to help people experience music. In this paper, we utilize the three-dimensional Resonance-Arousal-Valence emotion model to represent the emotions invoked by music, and the relationship between acoustic features and their emotional impact based on this model is established. In addition, we also consider the emotional tag features for music, and then represent acoustic features and emotional tag features jointly in a low dimensional embedding space for music emotion, while the joint emotion space is optimized by minimizing the joint loss of acoustic features and emotional tag features through dimension reduction. Finally we construct a unified framework for music retrieval in joint emotion space by the means of query-by-music or query-by-tag or together, and then we utilize our proposed ranking algorithm to return an optimized ranked list that has the highest emotional similarity. The experimental results show that the joint emotion space and unified framework can produce satisfying results for emotion-based music retrieval.

Original language	English
Title of host publication	Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings
Pages	524-534
Number of pages	11
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-35725-1_48
Publication status	Published - 2013
Event	19th International Conference on Advances in Multimedia Modeling, MMM 2013 - Huangshan, China Duration: 7 Jan 2013 → 9 Jan 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7732 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Advances in Multimedia Modeling, MMM 2013
Country/Territory	China
City	Huangshan
Period	7/01/13 → 9/01/13

User-Defined Keywords

Audio features
Dimensionality reduction
Emotional tag
Music emotion
Music retrieval
Ranking

Access to Document

10.1007/978-3-642-35725-1_48

Cite this

Deng, J. J., & Leung, C. H. C. (2013). Music retrieval in joint emotion space using audio features and emotional tags. In Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings (PART 1 ed., pp. 524-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7732 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-35725-1_48

Deng, James J. ; Leung, C. H.C. / Music retrieval in joint emotion space using audio features and emotional tags. Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings. PART 1. ed. 2013. pp. 524-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Emotion-based music retrieval provides a natural and humanized way to help people experience music. In this paper, we utilize the three-dimensional Resonance-Arousal-Valence emotion model to represent the emotions invoked by music, and the relationship between acoustic features and their emotional impact based on this model is established. In addition, we also consider the emotional tag features for music, and then represent acoustic features and emotional tag features jointly in a low dimensional embedding space for music emotion, while the joint emotion space is optimized by minimizing the joint loss of acoustic features and emotional tag features through dimension reduction. Finally we construct a unified framework for music retrieval in joint emotion space by the means of query-by-music or query-by-tag or together, and then we utilize our proposed ranking algorithm to return an optimized ranked list that has the highest emotional similarity. The experimental results show that the joint emotion space and unified framework can produce satisfying results for emotion-based music retrieval.",

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Deng, JJ & Leung, CHC 2013, Music retrieval in joint emotion space using audio features and emotional tags. in Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7732 LNCS, pp. 524-534, 19th International Conference on Advances in Multimedia Modeling, MMM 2013, Huangshan, China, 7/01/13. https://doi.org/10.1007/978-3-642-35725-1_48

Music retrieval in joint emotion space using audio features and emotional tags. / Deng, James J.; Leung, C. H.C.
Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings. PART 1. ed. 2013. p. 524-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7732 LNCS, No. PART 1).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Leung, C. H.C.

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N2 - Emotion-based music retrieval provides a natural and humanized way to help people experience music. In this paper, we utilize the three-dimensional Resonance-Arousal-Valence emotion model to represent the emotions invoked by music, and the relationship between acoustic features and their emotional impact based on this model is established. In addition, we also consider the emotional tag features for music, and then represent acoustic features and emotional tag features jointly in a low dimensional embedding space for music emotion, while the joint emotion space is optimized by minimizing the joint loss of acoustic features and emotional tag features through dimension reduction. Finally we construct a unified framework for music retrieval in joint emotion space by the means of query-by-music or query-by-tag or together, and then we utilize our proposed ranking algorithm to return an optimized ranked list that has the highest emotional similarity. The experimental results show that the joint emotion space and unified framework can produce satisfying results for emotion-based music retrieval.

AB - Emotion-based music retrieval provides a natural and humanized way to help people experience music. In this paper, we utilize the three-dimensional Resonance-Arousal-Valence emotion model to represent the emotions invoked by music, and the relationship between acoustic features and their emotional impact based on this model is established. In addition, we also consider the emotional tag features for music, and then represent acoustic features and emotional tag features jointly in a low dimensional embedding space for music emotion, while the joint emotion space is optimized by minimizing the joint loss of acoustic features and emotional tag features through dimension reduction. Finally we construct a unified framework for music retrieval in joint emotion space by the means of query-by-music or query-by-tag or together, and then we utilize our proposed ranking algorithm to return an optimized ranked list that has the highest emotional similarity. The experimental results show that the joint emotion space and unified framework can produce satisfying results for emotion-based music retrieval.

KW - Audio features

KW - Dimensionality reduction

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KW - Music retrieval

KW - Ranking

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Deng JJ, Leung CHC. Music retrieval in joint emotion space using audio features and emotional tags. In Advances in Multimedia Modeling - 19th International Conference, MMM 2013, Proceedings. PART 1 ed. 2013. p. 524-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-35725-1_48

Music retrieval in joint emotion space using audio features and emotional tags

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