A weakly-supervised discriminative model for audio-to-score alignment
Rémi Lajugie, Piotr Bojanowski, Philippe Cuvillier, Sylvain Arlot, Francis R. Bach
Abstract
In this paper, we consider a new discriminative approach to the problem of audio-to-score alignment. We consider two distinct informations provided by 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 only weak supervision. 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.
BibTeX
@inproceedings{icassp2016_aweaklysupervise,
title = {A weakly-supervised discriminative model for audio-to-score alignment},
author = {Rémi Lajugie and Piotr Bojanowski and Philippe Cuvillier and Sylvain Arlot and Francis R. Bach},
booktitle = {ICASSP 2016},
year = {2016}
}