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arXiv · 2105.04309

Multi-modal Conditional Bounding Box Regression for Music Score Following

Florian Henkel·Gerhard Widmer
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Abstract

This paper addresses the problem of sheet-image-based on-line audio-to-score alignment also known as score following. Drawing inspiration from object detection, a conditional neural network architecture is proposed that directly predicts x,y coordinates of the matching positions in a complete score sheet image at each point in time for a given musical performance. Experiments are conducted on a synthetic polyphonic piano benchmark dataset and the new method is compared to several existing approaches from the literature for sheet-image-based score following as well as an Optical Music Recognition baseline. The proposed approach achieves new state-of-the-art results and furthermore significantly improves the alignment performance on a set of real-world piano recordings by applying Impulse Responses as a data augmentation technique.

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BibTeXRIS

Florian Henkel, Gerhard Widmer. 2021-05-10. Multi-modal Conditional Bounding Box Regression for Music Score Following. https://arxiv.org/abs/2105.04309

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