  The identification of structural differences between a music performance and
the score is a challenging yet integral step of audio-to-score alignment, an
important subtask of music information retrieval. We present a novel method to
detect such differences between the score and performance for a given piece of
music using progressively dilated convolutional neural networks. Our method
incorporates varying dilation rates at different layers to capture both
short-term and long-term context, and can be employed successfully in the
presence of limited annotated data. We conduct experiments on audio recordings
of real performances that differ structurally from the score, and our results
demonstrate that our models outperform standard methods for structure-aware
audio-to-score alignment.
