Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment

• URL: http://arxiv.org/abs/2011.07546v1
• Date: Sun, 15 Nov 2020 14:58:03 GMT
• Title: Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment
• Authors: Ruchit Agrawal, Simon Dixon
• Abstract summary: We propose a method to overcome the limitation using learned frame similarity for audio-to-score alignment. We focus on offline audio-to-score alignment of piano music.
• Score: 13.269759433551478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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