Polyphonic Music Generation with Sequence Generative Adversarial Networks
This work addresses music generation for AI and creative applications, but it is incremental as it extends an existing method to a more complex representation.
The authors tackled polyphonic music generation by adapting sequence generative adversarial networks (SeqGAN) to handle chords and melodies with dynamic timings, resulting in musically coherent sequences with improved quantitative and qualitative measures.
We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of a monophonic melody generation suggested in the original work, we present an efficient representation of a polyphony MIDI file that simultaneously captures chords and melodies with dynamic timings. The proposed method condenses duration, octaves, and keys of both melodies and chords into a single word vector representation, and recurrent neural networks learn to predict distributions of sequences from the embedded musical word space. We experiment with the original method and the least squares method to the discriminator, which is known to stabilize the training of GANs. The network can create sequences that are musically coherent and shows an improved quantitative and qualitative measures. We also report that careful optimization of reinforcement learning signals of the model is crucial for general application of the model.