Detection of Music Mood for Context-aware Music Recommendation


KIPS Transactions on Software and Data Engineering, Vol. 17, No. 4, pp. 263-274, Apr. 2010
10.3745/KIPSTB.2010.17.4.263, Full Text:

Abstract

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people’s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.


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Cite this article
[IEEE Style]
J. I. Lee, D. G. Yeo and B. M. Kim, "Detection of Music Mood for Context-aware Music Recommendation," KIPS Journal B (2001 ~ 2012) , vol. 17, no. 4, pp. 263-274, 2010. DOI: 10.3745/KIPSTB.2010.17.4.263.

[ACM Style]
Jong In Lee, Dong Gyu Yeo, and Byeong Man Kim. 2010. Detection of Music Mood for Context-aware Music Recommendation. KIPS Journal B (2001 ~ 2012) , 17, 4, (2010), 263-274. DOI: 10.3745/KIPSTB.2010.17.4.263.