Learning music emotion primitives via supervised dynamic clustering

Yan Liu, Xiang Zhang, Gong Chen, Kejun Zhang, Yang LIU

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


This paper explores a fundamental problem in music emotion analysis, i.e., how to segment the music sequence into a set of basic emotive units, which are named as emotion primitives. Current works on music emotion analysis are mainly based on the fixedlength music segments, which often leads to the difficulty of accurate emotion recognition. Short music segment, such as an individual music frame, may fail to evoke emotion response. Long music segment, such as an entire song, may convey various emotions over time. Moreover, the minimum length of music segment varies depending on the types of the emotions. To address these problems, we propose a novel method dubbed supervised dynamic clustering (SDC) to automatically decompose the music sequence into meaningful segments with various lengths. First, the music sequence is represented by a set of music frames. Then, the music frames are clustered according to the valence-arousal values in the emotion space. The clustering results are used to initialize the music segmentation. After that, a dynamic programming scheme is employed to jointly optimize the subsequent segmentation and grouping in the music feature space. Experimental results on standard dataset show both the effectiveness and the rationality of the proposed method.

Original languageEnglish
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450336031
Publication statusPublished - 1 Oct 2016
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 15 Oct 201619 Oct 2016

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference


Conference24th ACM Multimedia Conference, MM 2016
Country/TerritoryUnited Kingdom

Scopus Subject Areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Software

User-Defined Keywords

  • Emotion primitives
  • Music emotion analysis
  • Supervised dynamic clustering


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