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1 Equipe Interactions musicales temps-réel STMS - Sciences et Technologies de la Musique et du Son

Abstract : This paper describes our attempt to make the Hidden Markov Model HMM score following system developed at Ircam sensible to past experiences in order to obtain better audio to score real-time alignment for musical applications. A new observation modeling based on Gaussian Mixture Models is developed which is trainable using a learning algorithm we would call automatic discriminative training. The novelty of this system lies in the fact that this method, unlike classical methods for HMM training, is not concerned with modeling the music signal but with correctly choosing the sequence of music events that was performed. Besides obtaining better alignment, new system-s parameters are controllable in a physical manner and the training algorithm learns different styles of music performance as discussed.

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Keywords : probabilistic modeling learning Gaussian Mixture Models performing training automatic accompaniment Hidden Markov Models score following

Mots-clés : Informatique musicale

Author: Arshia Cont - Diemo Schwarz - Norbert Schnell -



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