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Generating music has a few notable differences from generating images andvideos. First, music is an art of time, necessitating a temporal model. Second,music is usually composed of multiple instruments/tracks with their owntemporal dynamics, but collectively they unfold over time interdependently.Lastly, musical notes are often grouped into chords, arpeggios or melodies inpolyphonic music, and thereby introducing a chronological ordering of notes isnot naturally suitable. In this paper, we propose three models for symbolicmulti-track music generation under the framework of generative adversarialnetworks (GANs). The three models, which differ in the underlying assumptionsand accordingly the network architectures, are referred to as the jammingmodel, the composer model and the hybrid model. We trained the proposed modelson a dataset of over one hundred thousand bars of rock music and applied themto generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.A few intra-track and inter-track objective metrics are also proposed toevaluate the generative results, in addition to a subjective user study. Weshow that our models can generate coherent music of four bars right fromscratch (i.e. without human inputs). We also extend our models to human-AIcooperative music generation: given a specific track composed by human, we cangenerate four additional tracks to accompany it. All code, the dataset and therendered audio samples are available at https://salu133445.github.io/musegan/ .

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment