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A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
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DOI：10.1109/LSP.2021.3135192.png)
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Abstract
En 中文
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.
Keywords:
Synchronization
Task analysis
Convolution
Computer architecture
Hidden Markov models
Computational modeling
Predictive models
Performance-score synchronization
audio-to-score alignment
convolutional neural networks
multimodal data
time-series alignment
stand-alone self-attention
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