Expressive Music Performance Modeling

TitleExpressive Music Performance Modeling
Publication TypeMaster Thesis
Year of Publication2010
AuthorsNeocleous, A.
preprint/postprint documentstatic/media/Neocleous-Andreas-Master-Thesis-2010.pdf
AbstractMachine learning approaches to modelling emotions in music performances were investigated and presented in this thesis. In particular, we investigated how professional musicians encode emotions, such as happiness, sadness, anger, fear and sweetness, in violin and saxophone audio performances. Suitable melodic description features were extracted from audio recordings. Following that, we applied various machine learning techniques for training expressive performance models. A model was trained for each emotion considered. Finally, new expressive performances were synthesized from inexpressive melody descriptions (i.e. music scores) using the induced models and the result was perceptually evaluated by asking a number of people to listen, compare and evaluate to the computer generated performances. Several machine learning techniques for inducing the expressive models were systematically explored and we present the results.
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