# AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer

arXiv: https://arxiv.org/abs/2108.11213 · HTML: https://mlsanity.com/paper/2108.11213/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2021.

## Built on by (most cited first, 22 of 22)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls](https://mlsanity.com/paper/2307.10304/) (2023)
- [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://mlsanity.com/paper/2402.06178/) (2024)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls](https://mlsanity.com/paper/2402.09508/) (2024)
- [Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing](https://mlsanity.com/paper/2310.12404/) (2023)
- [Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling](https://mlsanity.com/paper/2310.16334/) (2023)
- [Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation](https://mlsanity.com/paper/2403.07995/) (2024)
- [MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks](https://mlsanity.com/paper/2208.14345/) (2022)
- [Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation](https://mlsanity.com/paper/2211.05543/) (2022)
- [ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement](https://mlsanity.com/paper/2502.04522/) (2025)
- [Segment-Factorized Full-Song Generation on Symbolic Piano Music](https://mlsanity.com/paper/2510.05881/) (2025)
- [SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton](https://mlsanity.com/paper/2604.25498/) (2026)
- [ViTex: Visual Texture Control for Multi-Track Symbolic Music Generation via Discrete Diffusion Models](https://mlsanity.com/paper/2603.01984/) (2026)
- [Unsupervised Lead Sheet Generation via Semantic Compression](https://mlsanity.com/paper/2310.10772/) (2023)
- [Emotion Manipulation Through Music -- A Deep Learning Interactive Visual Approach](https://mlsanity.com/paper/2406.08623/) (2024)
- [Analyzing Byte-Pair Encoding on Monophonic and Polyphonic Symbolic Music: A Focus on Musical Phrase Segmentation](https://mlsanity.com/paper/2410.01448/) (2024)
- [MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers](https://mlsanity.com/paper/2511.04376/) (2025)
- [D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheet](https://mlsanity.com/paper/2602.03523/) (2026)
- [Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval](https://mlsanity.com/paper/2602.15074/) (2026)
- [LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation](https://mlsanity.com/paper/2604.11052/) (2026)
- [FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration](https://mlsanity.com/paper/2607.17526/) (2026)

## Abstract (arXiv)

Accompaniment arrangement is a difficult music generation task involving intertwined constraints of melody, harmony, texture, and music structure. Existing models are not yet able to capture all these constraints effectively, especially for long-term music generation. To address this problem, we propose AccoMontage, an accompaniment arrangement system for whole pieces of music through unifying phrase selection and neural style transfer. We focus on generating piano accompaniments for folk/pop songs based on a lead sheet (i.e., melody with chord progression). Specifically, AccoMontage first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure learning-based approaches, AccoMontage introduces a novel hybrid pathway, in which rule-based optimization and deep learning are both leveraged to complement each other for high-quality generation. Experiments show that our model generates well-structured accompaniment with delicate texture, significantly outperforming the baselines.