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

By Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, Yi-Hsuan Yang. 2017

38 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Roger Wattenhofer
2. Gino Brunner
3. Yuyi Wang
4. Andres Konrad
5. Yonatan Belinkov
6. Fabian Kuhn
7. Vikas Kawadia
8. Aaron Zollinger

Citations in the corpus

• MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer (2018, background): “[10] have recently shown the efficacy of CNNbased GANs for music composition.”