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Improvement of Observation Modeling for Score Following




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Improvement of Observation Modeling for Score Following


Cont, Arshia (auteur)


University of Paris 6, IRCAM , 2004



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


Training the score follower, in the context of musical practice, is to adapt its parameters to improve performance for a certain score. To this aim, every parameter used in the system has to have direct physical interpretation or correlation with high-level desired parameters, in order to be trainable and controllable. This criteria has forced us to reconsider the design and approach in one of the main components of the score follower, the probability observation block. In order to this approach, we developed a criticism based on the notion of heuristics used in the design of the existing system at the beginning of this project with a look at empirical-synthetical sciences which score following research is a member. In his respect, we argue that the heuristics used in the design of the system has been considered in a late stage during the design and suggests an alternative approach in which heuristics will be used as the lowest level of information modeling and higher-level models used in the system would become an outcome of a series of derivation based on these heuristics modelings. A novel learning algorithm based on these views called automatic discriminative training was implemented which conforms to the practical criteria of a score following. 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. In this manner, using a discrimination process we attempt to model class boundaries rather than constructing an accurate model for each class. The discrimination knowledge is provided by an alternative algorithm, namely Yin developed by de Cheveigné and Kawahara (2002). Following the design of the training method, further experiments were undertaken to improve the response of system. For this purpose, every feature in the observation process of the system was studied and examined for correlation with high-level states and using the analysis results, modifications on the existing feature as well as a totally new feature were introduced to be used in the system. During evaluations, the system proved to be more stable and the results are improved compared to the previous system. Moreover, due to our design approach, the shortcomings of the current system have physical interpretations in the terms of current design and can be envisioned for further improvements, which was not the case with the previous system. Finally, the new concepts presented in this work, opens a new and more flexible view of score following for further research and improvements by arising an urgent need for a database of aligned sound and other research work which would lead the system towards better following.


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2010-02-25 01:00:00

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