Title: MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment,
Abstract: Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical notes are often grouped into chords, arpeggios or melodies in
polyphonic music, and thereby introducing a chronological ordering of notes is
not naturally suitable. In this paper, we propose three models for symbolic
multi-track music generation under the framework of generative adversarial
networks (GANs). The three models, which differ in the underlying assumptions
and accordingly the network architectures, are referred to as the jamming
model, the composer model and the hybrid model. We trained the proposed models
on a dataset of over one hundred thousand bars of rock music and applied them
to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
A few intra-track and inter-track objective metrics are also proposed to
evaluate the generative results, in addition to a subjective user study. We
show that our models can generate coherent music of four bars right from
scratch (i.e. without human inputs). We also extend our models to human-AI
cooperative music generation: given a specific track composed by human, we can
generate four additional tracks to accompany it. All code, the dataset and the
rendered audio samples are available at this https URL .