arXiv AI
Jul 14
BeatEdit: Symbolic Music Generation as Explicit Editing
arXiv:2607. 11124v1 Announce Type: cross Abstract: Music creation is fundamentally a process of revision.
From Prediction to Collaboration: Interactive Symbolic Music Analysis
arXiv:2607. 13587v1 Announce Type: cross Abstract: Automatic symbolic music analysis has made substantial progress, yet existing systems are typically designed for a single mode of use, such as full-score prediction, and therefore do not match the broader range of operations that arise in analysis workflows, including partial completion, local correction, and iterative refinement.
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arXiv Computation and Language
Sep 15
ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis
arXiv:2609.13291v1 Announce Type: cross Abstract: We present ScorePrompts, an interactive system in which users upload a score, receive natural-language descriptions of its musical structure, ask que...
arXiv Machine Learning
1d ago
CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions
Chordonomicon is a new dataset of over 666,000 song-level symbolic chord progressions, each annotated with structural parts such as verse, chorus, and bridge, as well as genre and release date. The dataset was compiled by scraping user-generated progressions from multiple sources and shows strong similarity to established prior datasets. The authors also provide a reproducible benchmark suite for next chord prediction, evaluating RNN, GRU, and LSTM models across various context windows and data scales, and find that structural part annotations consistently improve prediction performance.
arXiv AI
Aug 20
Whole-Piece Training for Symbolic Music Language Models via Full-Horizon Compressed Recurrence
The paper introduces Whole-Piece Training for Symbolic Music Language Models using Full-Horizon Compressed Recurrence (FHCR), which maintains the full temporal horizon of recurrent memory while compressing its key-value representation to fit GPU limits. An evaluation diagnostic, KV-Reset Context Utilization (KRCU), demonstrates that full-horizon models retain long-range context beyond local windows, whereas limiting recurrent memory weakens this dependence. FHCR thus preserves long-range context utilization while significantly reducing recurrent memory cost, enabling efficient whole-piece modeling.
arXiv Machine Learning
Aug 31
How Far Should Tokenization Go? Predictive Effectiveness and Relational Losslessness
The paper proposes the Effectiveness–Losslessness Framework to guide tokenization in domains beyond language, using predictive codelength as a criterion. It introduces two boundaries: the Fact–Token Boundary, where observable structure should be encoded into tokens, and the Token–State Boundary, where context‑dependent relations should remain for model state rather than being pre‑tokenized. Experiments on symbolic music show that making musical time explicit and applying tonal‑frame canonicalization improve predictive performance, while fixed pitch coordinates and reversible BPE can increase predictive code length, indicating that carrier compaction alone does not guarantee better predictions.
arXiv AI
Jul 23
Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
arXiv:2607. 20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes.
Hugging Face Trending Papers
Jul 22
Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The proposed framework supports three tasks: Lyrics-to-Song Generation, which generates complete songs from text descriptions, lyrics, and musical attributes; Instrumental Music Generation, which creates music without vocals; and Cover Song Generation, which reinterprets existing songs with different styles while preserving their melodic content.
arXiv AI
Jun 2
Multimodal Music Recommendation System using LLMs
arXiv:2606. 00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content.
arXiv Machine Learning
Jul 31
Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances
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.
arXiv Machine Learning
Jun 3
SketchSong: Hierarchical Song Generation with Sketch Planning and Fine-Grained Multi-Track Modeling
arXiv:2606. 03169v1 Announce Type: cross Abstract: Recent song generation systems can synthesize realistic audio, yet generating complete songs remains challenging for two reasons.
Hugging Face Trending Papers
Jul 30
Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances
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.
arXiv AI
Jun 30
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
arXiv:2606. 30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.