|Title:||Machine recognition of music emotion: A review||Authors:||Yang, Y.-H.
HOMER H. CHEN
|Keywords:||Music emotion recognition||Issue Date:||2012||Journal Volume:||3||Journal Issue:||3||Source:||ACM Transactions on Intelligent Systems and Technology||Abstract:||
The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research. © 2012 ACM 2157-6904/2012/05-ART40 $10.00.
|DOI:||10.1145/2168752.2168754||SDG/Keyword:||Digital music; Emotion recognition; Information access; Machine recognition; Model training; MP-3 players; Music information; Music information retrieval; Music signals; Visualization; Information retrieval|
|Appears in Collections:||電機工程學系|
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