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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 11

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.

By Dmitrii Gavrilev

rag diffusion

arXiv AI

Jun 24

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.

By Baisen Wang, Chenxi Bao, Qisong Han

diffusion efficiency

arXiv AI

Sep 17

CPR: Combining global composing, local performing and full-sequence refining in piano rendering with continuous autoregressive modelling

The paper introduces CPR, a piano rendering framework that combines continuous autoregressive modeling with local flow matching and full‑sequence refinement. It predicts continuous hidden states, generates 24 kHz acoustic latents, and upsamples to 48 kHz, while new techniques BREPA and MT‑RoPE enhance musical semantics and cross‑modal alignment.

By Chong Jing, Junan Zhang, Zhizheng Wu

diffusion efficiency safety

arXiv Machine Learning

Aug 6

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

arXiv:2608. 04378v1 Announce Type: cross Abstract: Collaborative music agents need internal representations rich enough to support both understanding and generation, yet flexible enough for a workflow where the human retains agency.

By Scott H. Hawley

llms rag agents

arXiv Machine Learning

Jul 31

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.

By Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro

rag safety

Hugging Face Trending Papers

Jul 30

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.

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Related stories

PianoKontext: Expressive Performance Rendering from Deadpan Context

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

CPR: Combining global composing, local performing and full-sequence refining in piano rendering with continuous autoregressive modelling

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