SyMuPe: Affective and Controllable Symbolic Music Performance
Ilya Borovik, Dmitrii Gavrilev, Vladimir Viro

TL;DR
SyMuPe introduces a novel affective and controllable symbolic piano performance framework using conditional flow matching, enabling expressive, emotion-aware music generation that rivals human performances and supports interactive applications.
Contribution
The paper presents PianoFlow, a new conditional flow matching model for expressive, emotion-controlled symbolic piano performance, trained on a large dataset and outperforming existing transformer-based models.
Findings
PianoFlow outperforms transformer baselines in quality.
The model achieves human-like performance levels.
Emotion control via text conditioning is effective.
Abstract
Emotions are fundamental to the creation and perception of music performances. However, achieving human-like expression and emotion through machine learning models for performance rendering remains a challenging task. In this work, we present SyMuPe, a novel framework for developing and training affective and controllable symbolic piano performance models. Our flagship model, PianoFlow, uses conditional flow matching trained to solve diverse multi-mask performance inpainting tasks. By design, it supports both unconditional generation and infilling of music performance features. For training, we use a curated, cleaned dataset of 2,968 hours of aligned musical scores and expressive MIDI performances. For text and emotion control, we integrate a piano performance emotion classifier and tune PianoFlow with the emotion-weighted Flan-T5 text embeddings provided as conditional inputs.…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception
