MuseGAN

MuseGAN is a project on music generation. In a nutshell, we aim to generate polyphonic music of multiple tracks (instruments). The proposed models are able to generate music either from scratch, or by accompanying a track given a priori by the user.

We train the model with training data collected from Lakh Pianoroll Dataset to generate pop song phrases consisting of bass, drums, guitar, piano and strings tracks.

Sample results are available here.

Papers

Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation
Hao-Wen Dong and Yi-Hsuan Yang
in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018.
[website] [arxiv] [paper] [slides(long)] [slides(short)] [poster] [code]

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Hao-Wen Dong,* Wen-Yi Hsiao,* Li-Chia Yang and Yi-Hsuan Yang, (*equal contribution)
in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
[website] [arxiv] [paper] [slides] [code]

MuseGAN: Demonstration of a Convolutional GAN Based Model for Generating Multi-track Piano-rolls
Hao-Wen Dong,* Wen-Yi Hsiao,* Li-Chia Yang and Yi-Hsuan Yang (*equal contribution)
in Late-Breaking Demos of the 18th International Society for Music Information Retrieval Conference (ISMIR), 2017. (two-page extended abstract)
[paper] [poster]