Homophonic Music Composition Using a GAN and LSTM Pipeline for Melody and Harmony Generation

Over the years, various efforts have been made to create music using procedural methods. However, few of these attempts have focused on generating structured music with melodies and chords that naturally complement each other. This research aims to develop a pipeline of neural networks to produce homophonic music, including a melody and accompanying chords, without external input. The goal is for the generated music to feature a natural-sounding melody with harmonies that provide structural coherence, akin to the approach of a human composer. To enhance the quality of the generated music, the research employs a Generative Adversarial Network (GAN) for melody generation and a hierarchical Long Short-Term Memory (LSTM) network for harmony generation. The GAN is used to produce melodies that exhibit natural motifs and variations, while the hierarchical LSTM is utilized to generate chord progressions that contextually support and enrich the melodies. This step-by-step approach ensures that the generated music is both structurally sound and expressive. By focusing on naturalness and consistency in music, this research aims to closely mimic human creativity. The symbolic models used are unconstrained by specific musical guidelines, allowing for the procedural generation of music that closely resembles human composition.