Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes,arXiv - CS - Multimedia

当前位置： X-MOL 学术 › arXiv.cs.MM › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes
arXiv - CS - Multimedia Pub Date : 2020-07-11 , DOI: arxiv-2007.07244
Xianchao Wu and Chengyuan Wang and Qinying Lei

Current state-of-the-art AI based classical music creation algorithms such as Music Transformer are trained by employing single sequence of notes with time-shifts. The major drawback of absolute time interval expression is the difficulty of similarity computing of notes that share the same note value yet different tempos, in one or among MIDI files. In addition, the usage of single sequence restricts the model to separately and effectively learn music information such as harmony and rhythm. In this paper, we propose a framework with two novel methods to respectively track these two shortages, one is the construction of time-valued note sequences that liberate note values from tempos and the other is the separated usage of four sequences, namely, former note on to current note on, note on to note off, pitch, and velocity, for jointly training of four Transformer-XL networks. Through training on a 23-hour piano MIDI dataset, our framework generates significantly better and hour-level longer music than three state-of-the-art baselines, namely Music Transformer, DeepJ, and single sequence-based Transformer-XL, evaluated automatically and manually.

更新日期：2020-07-15

点击分享查看原文

点击收藏

阅读更多本刊最新论文