  Audio-to-score alignment aims at generating an accurate mapping between a
performance audio and the score of a given piece. Standard alignment methods
are based on Dynamic Time Warping (DTW) and employ handcrafted features. We
explore the usage of neural networks as a preprocessing step for DTW-based
automatic alignment methods. Experiments on music data from different acoustic
conditions demonstrate that this method generates robust alignments whilst
being adaptable at the same time.
