arXiv:2609.05662v1 Announce Type: cross Abstract: Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of tr...
Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of complexity. However, the impact of this progress has been limited by the visual domains of available training datasets, which are largely born-digital.
arXiv:2607. 05769v1 Announce Type: cross Abstract: We propose a novel pipeline, Legato 2, for extracting symbolic notation and semantic knowledge from images of sheet music.
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.
arXiv:2606. 03169v1 Announce Type: cross Abstract: Recent song generation systems can synthesize realistic audio, yet generating complete songs remains challenging for two reasons.
InkSight is a system that converts offline handwritten notes captured in photos into digital ink, enabling seamless transition from pen-and-paper to editable, vectorized formats. Unlike prior methods that rely heavily on geometric image properties, InkSight integrates reading and writing priors, allowing it to learn from limited paired data. The model successfully derenders handwritten text in diverse photos and even simple sketches, with human evaluations showing 87% of outputs as valid tracings and 67% resembling human pen trajectories.
arXiv:2607. 19776v1 Announce Type: cross Abstract: Existing symbolic music generation models typically use bars as the basic structural unit.
A tutorial monograph on generative models for music audio covers a wide range of applications—from instrumental clips and full songs with vocals to edits of existing recordings and real‑time accompaniment systems. It explains neural audio codecs, autoencoders, autoregressive and masked token models, diffusion and flow matching, sampling acceleration, and preference optimization, linking training objectives to published systems through worked examples. The book also discusses musical requirements for different tasks, compares representations and methods, and addresses evaluation, data provenance, memorization, attribution, and legal implications.
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.
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.
arXiv:2607. 14537v1 Announce Type: cross Abstract: Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures.
ONOTE is a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences, focusing on omnimodal notation processing centered on sheet music. It introduces a test-only benchmark drawing from diverse musical sources—including staff, Jianpu, and tablature—across varied genres, instruments, and structural conditions, with aligned multimodal derivatives. The framework supports four complementary tasks—score understanding, notation conversion, audio transcription, and symbolic generation—while constructing a provenance-bearing proposition hypergraph from external music-theory materials for evidence retrieval and deterministic validity checks.