Research on Chord-Constrained Two-Track Music Generation Based on Improved GAN Networks

Chords have a role in music for emotional expression, and the generated melodies have more richness through the constraining effect of chords. In this paper, based on a GAN network music generation model based on chord features, a GRU network is used in chord feature extraction in order to autonomously learn chords at 1 : t − 1 moments and generate chords at t moments, by saving the hidden layer state of each batch and constructing a layer of GRU combined with a generator, thus achieving the effect of automatically learning the overall style of chords. The performance of the four models is gradually optimized by weighted averaging, and the melodic pleasantness generated by all four models has a significant positive correlation with musical coherence and creativity.