Machine Learning
Analysis of Rachmaninoff's Piano Performances Using Inductive Logic Programming (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
DS '02 Proceedings of the 5th International Conference on Discovery Science
Analysis and Prediction of Piano Performances Using Inductive Logic Programming
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Sound onset detection by applying psychoacoustic knowledge
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
A genetic rule-based model of expressive performance for jazz saxophone
Computer Music Journal
Expressive concatenative synthesis by reusing samples from real performance recordings
Computer Music Journal
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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.