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
Gangqinpu is a large, multi‑genre piano dataset that provides aligned audio, MIDI, and musical scores for 18,786 pieces, covering pop, classical, jazz, folk/traditional, rock, and blues. The dataset includes automatic note‑level alignments, beat/downbeat annotations, genre labels, and a binary classifier to filter non‑expressive performances. It is designed to reduce data leakage with ASAP splits and has shown cross‑dataset improvements in beat tracking and in‑domain gains for transcription and expressive performance rendering.
By Wei Zeng, Anvay Mathur, Ye Wang
arXiv:2606. 12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes.
By Dmitrii Gavrilev
MIDI-REPA is a framework that aligns symbolic music representations with intermediate layers of a flow-matching DiT model to improve text-to-music generation. By deriving MIDI supervision from automatic music transcription and aligning a MuScriptor-derived piano roll with an early DiT layer throughout the flow trajectory, the method achieves competitive performance on the ATTM objective metrics without extra training data or techniques. The approach is trainable on a single RTX 3090 in about 31 hours and is supported by released transcription pipeline, training code, and audio samples.
By Song-Ze Yu, Fang-Duo Tsai, Yi-Hsuan Yang
arXiv:2409. 14086v2 Announce Type: replace-cross Abstract: There have been several studies on automatically generating piano covers, and recent advancements in deep learning have enabled the creation of more sophisticated covers.
By Kazuma Komiya, Yoshihisa Fukuhara
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:2608. 03050v1 Announce Type: cross Abstract: What is music style?
By Jingwei Zhao, Gus Xia, Ziyu Wang, Ye Wang
DuET is a Dual-Encoder Transformer that jointly learns note-level piano performance-to-score transcription and frame-level beat/downbeat tracking. It uses bidirectional cross‑attention between note and frame encoders and an autoregressive decoder that alternates between their representations to predict structured score attributes. The model introduces TimeRoPE and Time‑Pitch Rotary Positional Encoding (TPRoPE) to share a performance‑time reference and encode relative temporal and pitch relationships, achieving significant error reductions on ASAP and Gangqinpu datasets.
By Wei Zeng, Ye Wang
arXiv:2607. 08168v1 Announce Type: cross Abstract: Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes.
By Simon Rouard, Michael Krause, Axel Roebel, Carl-Johann Simon-Gabriel, Alexandre D\'efossez
The paper demonstrates that a pretrained symbolic music transformer already encodes jazz pianist identity sufficiently for accurate classification across two benchmarks. By adding cross‑attention over learned pianist embeddings, the model can generate music conditioned on a specific artist’s style, and evaluation protocols confirm that the generated continuations are correctly attributed to the intended pianist. Additionally, the classifier is repurposed to identify the most characteristic moments in a performance, revealing the musical gestures that distinguish each pianist’s voice.
By Drew Edwards, Akira Maezawa, Simon Dixon
Neural music editing requires a system to perform a requested change while preserving the musical content that should remain unchanged. Existing evaluations often lack a paired target edit, making the...
The paper introduces MI-MIDI 2, an end‑to‑end protocol for discovering and evaluating sparse autoencoder (SAE) features in text‑to‑MIDI generation. It applies the protocol to the text2midi and MIDI‑LLM models, characterizing SAE features through correlations with musical metrics, instrument programs, and automated descriptions, and then testing them via direct intervention in generated MIDI. The study finds that certain SAE features—particularly those linked to instrument programs and note‑density adjustments—effectively steer the output, with evidence validated across multiple controls and independent prompts.
By Jakub Po\'cwiardowski, Mateusz Modrzejewski