A Multi-pass Algorithm for Accurate Audio-to-Score Alignment

Most current audio-to-score alignment algorithms work on the level of score time frames; i.e., they cannot differentiate between several notes occurring at the same discrete time within the score. This level of accuracy is sufficient for a variety of applications. However, for those that deal with, for example, musical expression analysis such microtimings might also be of interest. Therefore, we propose a method that estimates the onset times of individual notes in a post-processing step. Based on the initial alignment and a feature obtained by matrix factorization, those notes for which the confidence in the alignment is high are chosen as anchor notes. The remaining notes in between are revised, taking into account the additional information about these anchors and the temporal relations given by the score. We show that this method clearly outperforms a reference method that uses the same features but does not differentiate between anchor and non-anchor notes.

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