  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, which
cannot be adapted to different acoustic conditions. We propose a method to
overcome this limitation using learned frame similarity for audio-to-score
alignment. We focus on offline audio-to-score alignment of piano music.
Experiments on music data from different acoustic conditions demonstrate that
our method achieves higher alignment accuracy than a standard DTW-based method
that uses handcrafted features, and generates robust alignments whilst being
adaptable to different domains at the same time.
