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<p>Piano performance analysis demands sophisticated understanding of both acoustic outputs and physical gestures, yet existing computational approaches typically treat these modalities in isolation. This study presents a novel framework that leverages generative adversarial networks to jointly model piano fingering correction and performance expressiveness through integrated audio-visual analysis. We develop a hierarchical attention-based fusion mechanism that learns adaptive correspondences between acoustic events and hand movements, combined with a dual-stream GAN architecture that concurrently handles fingering classification and expressiveness assessment. Experiments on a multimodal dataset comprising 3,847 performances demonstrate that our approach achieves 89.7% frame-level fingering accuracy and maintains correlation coefficients exceeding 0.85 for dynamic expressiveness features, substantially outperforming single-modality baselines. The system provides near real-time feedback with 180ms processing latency per second of audio, enabling practical deployment in interactive music education environments. These results validate the efficacy of cross-modal deep learning for capturing the coupled relationship between biomechanical execution and artistic interpretation in skilled musical performance.</p>

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Multimodal generative adversarial networks for piano fingering correction and performance expressiveness modeling through audio-visual feature fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Junyu Li

摘要

Piano performance analysis demands sophisticated understanding of both acoustic outputs and physical gestures, yet existing computational approaches typically treat these modalities in isolation. This study presents a novel framework that leverages generative adversarial networks to jointly model piano fingering correction and performance expressiveness through integrated audio-visual analysis. We develop a hierarchical attention-based fusion mechanism that learns adaptive correspondences between acoustic events and hand movements, combined with a dual-stream GAN architecture that concurrently handles fingering classification and expressiveness assessment. Experiments on a multimodal dataset comprising 3,847 performances demonstrate that our approach achieves 89.7% frame-level fingering accuracy and maintains correlation coefficients exceeding 0.85 for dynamic expressiveness features, substantially outperforming single-modality baselines. The system provides near real-time feedback with 180ms processing latency per second of audio, enabling practical deployment in interactive music education environments. These results validate the efficacy of cross-modal deep learning for capturing the coupled relationship between biomechanical execution and artistic interpretation in skilled musical performance.