# D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheet

arXiv: https://arxiv.org/abs/2602.03523 · HTML: https://mlsanity.com/paper/2602.03523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 25 of its 29 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 25 of 25)

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- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Neighborhood Attention Transformer](https://mlsanity.com/paper/2204.07143/) (2022)
- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [Symbolic Music Generation with Diffusion Models](https://mlsanity.com/paper/2103.16091/) (2021)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [PopMAG: Pop Music Accompaniment Generation](https://mlsanity.com/paper/2008.07703/) (2020)
- [Dilated Neighborhood Attention Transformer](https://mlsanity.com/paper/2209.15001/) (2022)
- [The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures](https://mlsanity.com/paper/2008.01307/) (2020)
- [Learning Interpretable Representation for Controllable Polyphonic Music Generation](https://mlsanity.com/paper/2008.07122/) (2020)
- [Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls](https://mlsanity.com/paper/2307.10304/) (2023)
- [Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models](https://mlsanity.com/paper/2405.09901/) (2024)
- [AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer](https://mlsanity.com/paper/2108.11213/) (2021)
- [Automatic Analysis and Influence of Hierarchical Structure on Melody, Rhythm and Harmony in Popular Music](https://mlsanity.com/paper/2010.07518/) (2020)
- [Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach](https://mlsanity.com/paper/2209.08212/) (2022)
- [Discrete Diffusion Probabilistic Models for Symbolic Music Generation](https://mlsanity.com/paper/2305.09489/) (2023)
- [GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework](https://mlsanity.com/paper/2305.10841/) (2023)
- [Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models](https://mlsanity.com/paper/2310.14044/) (2023)
- [D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription](https://mlsanity.com/paper/2501.05068/) (2025)
- [Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation](https://mlsanity.com/paper/2410.08435/) (2024)

## Abstract (arXiv)

Generating piano accompaniments in the symbolic music domain is a challenging task that requires producing a complete piece of piano music from given melody and chord constraints, such as those provided by a lead sheet. In this paper, we propose a discrete diffusion-based piano accompaniment generation model, D3PIA, leveraging local alignment between lead sheet and accompaniment in piano-roll representation. D3PIA incorporates Neighborhood Attention (NA) to both encode the lead sheet and condition it for predicting note states in the piano accompaniment. This design enhances local contextual modeling by efficiently attending to nearby melody and chord conditions. We evaluate our model using the POP909 dataset, a widely used benchmark for piano accompaniment generation. Objective evaluation results demonstrate that D3PIA preserves chord conditions more faithfully compared to continuous diffusion-based and Transformer-based baselines. Furthermore, a subjective listening test indicates that D3PIA generates more musically coherent accompaniments than the comparison models.