The Role of Emotion and Context in Musical Preference.
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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.
Authors
Song, YadingCollections
- Theses [4492]