1 code implementation • 19 Sep 2023 • Xinda Wu, Zhijie Huang, Kejun Zhang, Jiaxing Yu, Xu Tan, Tieyao Zhang, ZiHao Wang, Lingyun Sun
In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0. 82, 0. 87, 0. 78, and 0. 94 in consistency, rhythmicity, structure, and overall quality, respectively.
1 code implementation • 11 Jan 2023 • Kejun Zhang, Xinda Wu, Tieyao Zhang, Zhijie Huang, Xu Tan, Qihao Liang, Songruoyao Wu, Lingyun Sun
Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally.