arXiv AI June 26, 2026 By Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li

Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training

arXiv:2512. 02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

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PianoKontext: Expressive Performance Rendering from Deadpan Context

arXiv:2606. 12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes.

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Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation

arXiv:2606. 24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain due to its prohibitive inference latency and offline rendering paradigm.

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Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

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Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes. However, these methods often disregard dependencies between notes, which poses a potential limitation in assessing the similarity between two sets of performances.

rag safety

arXiv Machine Learning

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arXiv:2607. 14537v1 Announce Type: cross Abstract: Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures.

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Related stories

PianoKontext: Expressive Performance Rendering from Deadpan Context

Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation

Helping Music Co-Creation Agents 'Listen' Well: Hierarchical Self-Supervised World Models for Understanding and Generation

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music