A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization

• URL: http://arxiv.org/abs/2204.08822v1
• Date: Tue, 19 Apr 2022 11:41:21 GMT
• Title: A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
• Authors: Ruchit Agrawal, Daniel Wolff, Simon Dixon
• Abstract summary: Performance-score synchronization is an integral task in signal processing. Traditional synchronization methods compute alignment using knowledge-driven approaches. We present a novel data-driven method for structure-score synchronization.
• Score: 12.951369232106178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Performance-score synchronization is an integral task in signal processing, which entails generating an accurate mapping between an audio recording of a performance and the corresponding musical score. Traditional synchronization methods compute alignment using knowledge-driven and stochastic approaches, and are typically unable to generalize well to different domains and modalities. We present a novel data-driven method for structure-aware performance-score synchronization. We propose a convolutional-attentional architecture trained with a custom loss based on time-series divergence. We conduct experiments for the audio-to-MIDI and audio-to-image alignment tasks pertained to different score modalities. We validate the effectiveness of our method via ablation studies and comparisons with state-of-the-art alignment approaches. We demonstrate that our approach outperforms previous synchronization methods for a variety of test settings across score modalities and acoustic conditions. Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches.

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