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Slides for ICASSP 2021 paper on structure-aware alignment

Citation Author(s):
Ruchit Agrawal, Daniel Wolff, Simon Dixon
Submitted by:
Ruchit Agrawal
Last updated:
2 July 2021 - 1:44am
Document Type:
Presentation Slides
Document Year:
Ruchit Agrawal
Paper Code:

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 signal processing. 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.

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