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Yung-Ching Tseng

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Conference Jul 2026

Style-aware data augmentation for deep learning on symbolic music

A style-aware data augmentation framework that combines rule-based design with statistical constraints, applied to small-scale, highly constrained Jiangnan symbolic music generation tasks is proposed, demonstrating the need for carefully designed augmentation strategies in highly constrained symbolic music generation tasks.

Yung-Ching Tseng, Yu-Chia Wang, Yung-An Chen et al. · 0 citations

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