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A great number of deep learning based models have been recently proposed forautomatic music composition. Among these models, the Transformer stands out asa prominent approach for generating expressive classical piano performance witha coherent structure of up to one minute. The model is powerful in that itlearns abstractions of data on its own, without much human-imposed domainknowledge or constraints. In contrast with this general approach, this papershows that Transformers can do even better for music modeling, when we improvethe 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, sothat Transformers can be more easily aware of the beat-bar-phrase hierarchicalstructure in music. The new data representation maintains the flexibility oflocal tempo changes, and provides hurdles to control the rhythmic and harmonicstructure of music. With this approach, we build a Pop Music Transformer thatcomposes Pop piano music with better rhythmic structure than existingTransformer models.

Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions