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Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance

Dasaem Jeong · Taegyun Kwon · Yoojin Kim · Juhan Nam

Pacific Ballroom #246

Keywords: [ Time Series and Sequence Models ] [ Other Applications ] [ Deep Generative Models ]


Music score is often handled as one-dimensional sequential data. Unlike words in a text document, notes in music score can be played simultaneously by the polyphonic nature and each of them has its own duration. In this paper, we represent the unique form of musical score using graph neural network and apply it for rendering expressive piano performance from the music score. Specifically, we design the model using note-level gated graph neural network and measure-level hierarchical attention network with bidirectional long short-term memory with an iterative feedback method. In addition, to model different styles of performance for a given input score, we employ a variational auto-encoder. The result of the listening test shows that our proposed model generated more human-like performances compared to a baseline model and a hierarchical attention network model that handles music score as a word-like sequence.

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