Paper Info
Title
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
Abstract
A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model. In particular, we seek to impose a metrical structure in the input data, so that Transformers can be more easily aware of the beat-bar-phrase hierarchical structure in music. The new data representation maintains the flexibility of local tempo changes, and provides hurdles to control the rhythmic and harmonic structure of music. With this approach, we build a Pop Music Transformer that composes Pop piano music with better rhythmic structure than existing Transformer models.
Year
DOI
Venue
2020
10.1145/3394171.3413671
MM '20: The 28th ACM International Conference on Multimedia Seattle WA USA October, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7988-5
4
PageRank 
References 
Authors
0.71
0
2
Name
Order
Citations
PageRank
Yu-Siang Huang1112.56
Yi-Hsuan Yang2102284.71