arXiv:2608. 03419v1 Announce Type: cross Abstract: Audio-based piano transcription performs well on onset, pitch, and velocity, but the sustain pedal lets sound persist long after key release, so audio systems predict pedal-extended offsets rather than physical key release.
By Yonghyun Kim, Hoyeol Sohn, Juhan Nam, Alexander Lerch
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
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
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:2610.35816v1 Announce Type: cross
Abstract: Editing multi-camera concerts requires coordinated decisions about musical relevance, visual quality and cut timing. We present a modular framework t...
By Lennart Schiweck, Adrian Kempf, Michelle Retig, Moritz Feucht, Tim Mangliers, Benjamin Himpel
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