modeling emotions in violin audio recordings
In this paper we present a machine learning approach to modeling emotions in music performances. In particular, we investigate how a professional musician encodes emotions, such as happiness, sadness, anger and fear, in violin audio performances. In order to apply machine learning techniques to our data we first extract a melodic description from the audio recordings. We then train a model for each emotion considered. Finally, we synthesize new expressive performances from inexpressive melody descriptions (i.e. music scores) using the induced models. We explore and compare several machine learning techniques for inducing the expressive models and present the results.
audio machine learning music expressive performance signal processing