DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : In this paper, we consider a new discriminative approach to the problem of audio-to-score alignment. We consider the two distinct informations provided by the music scores: (i) an exact ordered list of musical events and (ii) an approximate prior information about relative duration of events. We extend the basic dynamic time warping algorithm to a convex problem that learns optimal classifiers for all events while jointly aligning files, using this weak supervision only. We show that the relative duration between events can be easily used as a penalization of our cost function and allows us to drastically improve performances of our approach. We demonstrate the validity of our approach on a large and realistic dataset.
41st International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar 2016, Shanghai, China. Proceedings of the 41st International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 〈http://www.icassp2016.org/〉
https://hal.archives-ouvertes.fr/hal-01251018
Contributeur : Philippe Cuvillier
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Soumis le : mardi 5 janvier 2016 - 15:12:06
Dernière modification le : lundi 29 mai 2017 - 14:23:41
Document(s) archivé(s) le : jeudi 7 avril 2016 - 15:26:31
Rémi Lajugie, Piotr Bojanowski, Philippe Cuvillier, Sylvain Arlot, Francis Bach. A weakly-supervised discriminative model for audio-to-score alignment. 41st International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar 2016, Shanghai, China. Proceedings of the 41st International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 〈http://www.icassp2016.org/〉. 〈hal-01251018〉
