Motif-augmented classical music synthesis via recurrent neural networks

We propose a motif-augmented approach to classical music synthesis using LSTM-based recurrent neural networks trained on J. S. Bach violin compositions.By combining motif augmentation with temperature-based sampling, we improve the entropy alignment between generated sequences and ground-truth data.Our experiments show that motif augmentation significantly reduces entropy deviation and enhances sequence coherence, as confirmed by statistical analysis.This method advances generative music modeling, offering potential applications in music composition and sequence prediction tasks.