  Audio-to-score alignment is an important pre-processing step for in-depth
analysis of classical music. In this paper, we apply novel
transposition-invariant audio features to this task. These low-dimensional
features represent local pitch intervals and are learned in an unsupervised
fashion by a gated autoencoder. Our results show that the proposed features are
indeed fully transposition-invariant and enable accurate alignments between
transposed scores and performances. Furthermore, they can even outperform
widely used features for audio-to-score alignment on `untransposed data', and
thus are a viable and more flexible alternative to well-established features
for music alignment and matching.
