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Title: Machine learning analysis of the cultural and cross-cultural aspects of beauty in music
Author: Q, Claire Elizabeth
Awarding Body: Aberystwyth University
Current Institution: Aberystwyth University
Date of Award: 2013
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Can machine learning algorithms be trained to recognise beauty in music? To what extent is human recognition of beauty in music cultural, or crosscultural? Music is prevalent in all human cultures. Music information retrieval is a growing eld in which computational techniques have been applied to many musical problems such as genre recognition and measuring musical similarity. Computational ethnomusicology is rarer because the acquisition of non-Western music is di cult. Beauty in music has been little investigated with scienti c methods, though there are some examples on which this thesis builds. The e ect of timbral and 12-step chroma audio features, and a wide variety of di erent machine learning algorithms techniques were tested, with the combination of all the MARSYAS features and Support Vector Machines performing well. Predicting beauty was rst investigated with a small set and later with a larger world music survey with Singaporean participants. Beauty was predicted based on a small selection of tags with good accuracy. Beauty ratings from the survey, conducted in Singapore, were predictable by machine learning using similar methods. Predicting the geographical origin of world music from audio features was attempted. Some promising results emerged, and novel methods for predicting points on the surface of the Earth were developed. An investigation into the link between beauty ratings and location was conducted. The Singaporean beauty ratings were predicted from audio content, geographic content and a combination of both, showing strong correlations between longitude, distance, and timbral features with the beauty ratings, which were statistically very closely linked with distance from Singapore. From this beauty in music is concluded to be culturally related and timbre is shown to be a good pointer to cultural differences.
Supervisor: Neal, Mark ; Liakata, Maria Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available