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Title: The role of emotion and context in musical preference
Author: Song, Yading
ISNI:       0000 0004 7962 4560
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2016
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The powerful emotional effects of music increasingly attract the attention of music information retrieval researchers and music psychologists. In the past decades, a gap exists between these two disciplines, and researchers have focused on different aspects of emotion in music. Music information retrieval researchers are concerned with computational tasks such as the classifcation of music by its emotional content, whereas music psychologists are more interested in the understanding of emotion in music. Many of the existing studies have investigated the above issues in the context of classical music, but the results may not be applicable to other genres. This thesis focusses on musical emotion in Western popular music combining knowledge from both disciplines. I compile a Western popular music emotion dataset based on online social tags, and present a music emotion classifcation system using audio features corresponding to four diferent musical dimensions. Listeners' perceived and induced emotional responses to the emotion dataset are compared, and I evaluate the reliability of emotion tags with listeners' ratings of emotion using two dominant models of emotion, namely the categorical and the dimensional emotion models. In the next experiment, I build a dataset of musical excerpts identi ed in a questionnaire, and I train my music emotion classifcation system with these audio recordings. I compare the di erences and similarities between the emotional responses of listeners and the results from automatic classifcation. Music emotions arise in complex interactions between the listener, the music, and the situation. In the final experiments, I explore the functional uses of music and musical preference in everyday situations. Specifcally, I investigate emotional uses of music in diferent music-listening situational contexts. Finally, I discuss the use of emotion and context in the future design of subjective music recommendation systems and propose the study of musical preference using musical features.
Supervisor: Not available Sponsor: China Scholarship Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Electronic Engineering and Computer Science ; Centre for Digital Music ; music emotion classi cation