SVR-based music mood classification and context-based music recommendation

Seungmin Rho; Byeong Jun Han; Eenjun Hwang

doi:10.1145/1631272.1631395

SVR-based music mood classification and context-based music recommendation

Seungmin Rho^*, Byeong Jun Han, Eenjun Hwang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

46 Citations (Scopus)

Abstract

With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.

Original language	English
Title of host publication	MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages	713-716
Number of pages	4
DOIs	https://doi.org/10.1145/1631272.1631395
Publication status	Published - 2009
Event	17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China Duration: 2009 Oct 19 → 2009 Oct 24

Publication series

Name	MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Other

Other	17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
Country/Territory	China
City	Beijing
Period	09/10/19 → 09/10/24

Keywords

Classification
Music mood
Ontology
Recommendation
Support vector regression

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/1631272.1631395

Cite this

Rho, S., Han, B. J., & Hwang, E. (2009). SVR-based music mood classification and context-based music recommendation. In MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums (pp. 713-716). (MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums). https://doi.org/10.1145/1631272.1631395

SVR-based music mood classification and context-based music recommendation. / Rho, Seungmin; Han, Byeong Jun; Hwang, Eenjun.
MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums. 2009. p. 713-716 (MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rho, S, Han, BJ & Hwang, E 2009, SVR-based music mood classification and context-based music recommendation. in MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums. MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums, pp. 713-716, 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums, Beijing, China, 09/10/19. https://doi.org/10.1145/1631272.1631395

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abstract = "With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8\% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.",

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SVR-based music mood classification and context-based music recommendation

Abstract

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Keywords

ASJC Scopus subject areas

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