arXiv:2606. 10627v1 Announce Type: cross Abstract: The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on.
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.
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.
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.
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.
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...
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.
Generating natural-language coaching feedback on motor skills can accelerate learning, yet expert coaches are scarce and expensive. Existing reference-based methods require expert demonstrations at both training and inference time, limiting practical deployment.
Piano2Pop is a music‑expansion task that converts piano MIDI into full instrumental pop productions. The method uses a pseudo‑supervised approach that conditions a pretrained flow‑matching model on piano rolls, trained on synthetic piano‑instrumental pairs derived from pop recordings. Experiments on 476 real‑world piano‑cover/pop pairs show that a pair‑supervised piano‑cover generator outperforms zero‑shot baselines, while function‑selective transcription (FST) reduces piano‑heavy outputs in pair‑free training and remains a promising alternative.
MotionCoach is a multimodal agent designed to enhance video editing models for professional motion editing, such as sports and martial arts. It generates grounded prompts and reference clips through multi‑turn tool use and self‑verification, enabling precise motion guidance without retraining the base editing models. The system is trained on a curated dataset and evaluated on VPMEBench, achieving significant score improvements on benchmark datasets.
arXiv:2608.30872v1 Announce Type: new Abstract: Objective assessment of surgical technical skill is important for surgical training and structured feedback, but current workflows remain dependent on...
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.
MotionCoach is a multimodal agent designed to generate precise guidance for professional motion editing in videos. It constructs grounded prompts and reference clips through multi‑turn tool use and self‑verification, leveraging a training pipeline that includes a 10k SFT dataset and a 2k RL dataset optimized with Group Relative Policy Optimization. Evaluation on the VPMEBench benchmark shows significant performance gains, raising overall scores from 55.1 to 65.6 on Seedance 2.0 and from 51.2 to 64.5 on MiniMax‑H3.