Music Generation Based on Bidirectional GRU Model

Abstract

Lately, substantial advancements in the realm of deep learning have given rise to new approaches for autonomously generating music. This study has devised a generative framework intended to produce musical melodies. This framework capitalizes on bidirectional gated recurrent units (GRU) as its foundational architecture. To impart knowledge to the model, a collection of classical piano compositions in MIDI format has been employed as the training dataset. One implements a stacked architecture of bidirectional GRU layers to capture long-term musical patterns. The addition of dropout regularization prevents overfitting. Generated samples are evaluated both quantitatively through model loss, as well as qualitatively by manual listening tests. According to the analysis, the approach can produce coherent musical melodies with reasonable structure. This demonstrates the potential for deep bidirectional models to learn musical syntax and generate new compositions. Limitations include lack of long-term musical form and repetitive patterns. Future work should explore architectures to improve coherence over longer time spans, as well as integrate other modalities like rhythm and harmony. These results provide a strong foundation for automated music generation systems.