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Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences

Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences

Sanghoon Jun, Seungmin Rho, Eenjun Hwang
Copyright: © 2010 |Volume: 6 |Issue: 2 |Pages: 16
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781609603953|DOI: 10.4018/jswis.2010040101
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MLA

Jun, Sanghoon, et al. "Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences." IJSWIS vol.6, no.2 2010: pp.1-16. http://doi.org/10.4018/jswis.2010040101

APA

Jun, S., Rho, S., & Hwang, E. (2010). Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences. International Journal on Semantic Web and Information Systems (IJSWIS), 6(2), 1-16. http://doi.org/10.4018/jswis.2010040101

Chicago

Jun, Sanghoon, Seungmin Rho, and Eenjun Hwang. "Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences," International Journal on Semantic Web and Information Systems (IJSWIS) 6, no.2: 1-16. http://doi.org/10.4018/jswis.2010040101

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Abstract

A typical music clip consists of one or more segments with different moods and such mood information could be a crucial clue for determining the similarity between music clips. One representative mood has been selected for music clip for retrieval, recommendation or classification purposes, which often gives unsatisfactory result. In this paper, the authors propose a new music retrieval and recommendation scheme based on the mood sequence of music clips. The authors first divide each music clip into segments through beat structure analysis, then, apply the k-medoids clustering algorithm for grouping all the segments into clusters with similar features. By assigning a unique mood symbol for each cluster, one can transform each music clip into a musical mood sequence. For music retrieval, the authors use the Smith-Waterman (SW) algorithm to measure the similarity between mood sequences. However, for music recommendation, user preferences are retrieved from a recent music playlist or user interaction through the interface, which generates a music recommendation list based on the mood sequence similarity. The authors demonstrate that the proposed scheme achieves excellent performance in terms of retrieval accuracy and user satisfaction in music recommendation.

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