mlsanityData to 7 Oct 2026

Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation

arXiv 2410.08435 ↗First posted 11 Oct 2024cs.SD cs.AI cs.LGcited by 1

On the map: Diffusion models → Audio, speech and music

Abstract (arXiv)

Developing generative models to create or conditionally create symbolic music presents unique challenges due to the combination of limited data availability and the need for high precision in note pitch. To address these challenges, we introduce an efficient Fine-Grained Guidance (FGG) approach within diffusion models. FGG guides the diffusion models to generate music that aligns more closely with the control and intent of expert composers, which is critical to improve the accuracy, listenability, and quality of generated music. This approach empowers diffusion models to excel in advanced applications such as improvisation, and interactive music creation. We derive theoretical characterizations for both the challenges in symbolic music generation and the effects of the FGG approach. We provide numerical experiments and subjective evaluation to demonstrate the effectiveness of our approach. We have published a demo page to showcase performances, which enables real-time interactive generation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 34 of its 42 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

34 of 42 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models16 May
  2. 2024Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion22 Feb
  3. 2024DITTO: Diffusion Inference-Time T-Optimization for Music Generation22 Jan
  4. 2023Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls19 Jul

Other references · 30

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  10. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  11. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  12. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 640
18 more
  1. 2022Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptionscited by 440
  2. 2023Common Diffusion Noise Schedules and Sample Steps are Flawedcited by 308
  3. 2022SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Modelcited by 276
  4. 2023MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizercited by 260
  5. 2018Music Transformercited by 207
  6. 2021Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphscited by 109
  7. 2020POP909: A Pop-song Dataset for Music Arrangement Generationcited by 83
  8. 2023Reflected Diffusion Modelscited by 81
  9. 2020PopMAG: Pop Music Accompaniment Generationcited by 68
  10. 2023Diffusion Models for Constrained Domainscited by 60
  11. 2024Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theorycited by 58
  12. 2020Learning Interpretable Representation for Controllable Polyphonic Music Generationcited by 40
  13. 2021MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAEcited by 39
  14. 2019Encoding Musical Style with Transformer Autoencoderscited by 35
  15. 2020Automatic Analysis and Influence of Hierarchical Structure on Melody, Rhythm and Harmony in Popular Musiccited by 21
  16. 2023GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Frameworkcited by 13
  17. 2022SDMuse: Stochastic Differential Music Editing and Generation via Hybrid Representationcited by 11
  18. 2019Exploring Conditioning for Generative Music Systems with Human-Interpretable Controlscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheetcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conditional Diffusion as Latent Constraints for Controllable Symbolic Music Generation
  2. 2024Why Perturbing Symbolic Music is Necessary: Fitting the Distribution of Never-used Notes through a Joint Probabilistic Diffusion Model
  3. 2024Multi-Track MusicLDM: Towards Versatile Music Generation with Latent Diffusion Model
  4. 2026Diff-Symbo: Text-Controlled Long-Duration Symbolic Music Generation Using Autoregressive Latent Diffusion Model
  5. 2026Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation
  6. 2025ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
  7. 2023Generating symbolic music using diffusion models
  8. 2025PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation