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
arXiv:2606. 12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes.
By Dmitrii Gavrilev
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
arXiv:2607. 11124v1 Announce Type: cross Abstract: Music creation is fundamentally a process of revision.
By Haoyu Gu, Lekai Qian, Haowu Zhou, Qi Liu, Shuai Wang
arXiv:2607. 09973v1 Announce Type: cross Abstract: Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows.
By M\'elodie Desbos, Yara Bahram, Eric Granger, Mohammadhadi Shateri
arXiv:2610.00691v1 Announce Type: new
Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed trac...
By Zhuo Ning, AmirHossein Naghi Razlighi, Sagi Polaczek, Daniel Cohen-Or, Ali Mahdavi-Amiri
arXiv:2606. 03803v1 Announce Type: cross Abstract: We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints.
By Marco Pasini, Javier Nistal, Mathias Rose Bjare, Stefan Lattner, George Fazekas
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.
By Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li
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
The paper introduces MuseCPEval, the first comprehensive framework for assessing Music Context Preservation (MuseCP) in music editing systems. It defines four categories of music facets and provides fine‑grained metrics to detect subtle changes during editing tasks such as timbre transfer, instrument substitution, and genre transformation. The authors validate the metrics objectively and through a human study, and demonstrate their practical use in evaluating diverse editing systems, offering insights into each system’s strengths and limitations.
By Yash Vishe, Eric Xue, Xunyi Jiang, Zachary Novack, Junda Wu, Julian McAuley, Xin Xu
arXiv:2608. 09035v1 Announce Type: cross Abstract: Text-to-music generation has advanced rapidly, but current systems still rely primarily on global text prompts, leaving the structural organization of generated music implicit and difficult to inspect, control, or revise before audio generation.
By Shuyu Li, Kejun Zhang, Jiahe Lei, Shulei Ji, Zihao Wang, Jiaxing Yu, Wanying Wu, Lei Wang
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.