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Implicit Learning of Musical Performance Parameters : Training Ircam's Score Follower

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text
 

Genre(s)

article
 

Forme(s)

document numérique
 

Cette ressource est disponible chez l'organisme suivant : Ircam - Centre Pompidou

Identification

Titre

Implicit Learning of Musical Performance Parameters
 

Sous-Titre

Training Ircam's Score Follower
 

Nom(s)

Cont, Arshia (auteur)
 
Schwarz, Diemo (auteur)
 
Schnell, Norbert (auteur)
 

Publication

Washington, USA , 2004
 

Description

Sujet(s)

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

Résumé

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 adapt itself to a certain style of performance of musicians on a particular piece. We focus mostly on the aspects of the implemented machine learning technic pertaining to the style of performance of the score follower. To this end, a new observation modeling based on Gaussian Mixture Models is developed which is trainable using a novel 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.
 

Note(s)

Contribution au colloque ou congrès : AAAI Symposium 2004 Style and Meaning in Language, Art, Music, and Design
 

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Date de la notice

2010-02-25 01:00:00
 

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