# Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls

arXiv: https://arxiv.org/abs/2402.09508 · HTML: https://mlsanity.com/paper/2402.09508/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://mlsanity.com/paper/2304.15010/) (2023)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [Mousai: Text-to-Music Generation with Long-Context Latent Diffusion](https://mlsanity.com/paper/2301.11757/) (2023)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [VampNet: Music Generation via Masked Acoustic Token Modeling](https://mlsanity.com/paper/2307.04686/) (2023)
- [Vision-Infused Deep Audio Inpainting](https://mlsanity.com/paper/1910.10997/) (2019)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls](https://mlsanity.com/paper/2307.10304/) (2023)
- [Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm](https://mlsanity.com/paper/2008.01291/) (2020)
- [AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer](https://mlsanity.com/paper/2108.11213/) (2021)
- [GACELA -- A generative adversarial context encoder for long audio inpainting](https://mlsanity.com/paper/2005.05032/) (2020)
- [Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding](https://mlsanity.com/paper/2108.05064/) (2021)
- [Inpainting of long audio segments with similarity graphs](https://mlsanity.com/paper/1607.06667/) (2016)

## Built on by (most cited first, 17 of 17)

- [ComposerX: Multi-Agent Symbolic Music Composition with LLMs](https://mlsanity.com/paper/2404.18081/) (2024)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [LLMs Meet Multimodal Generation and Editing: A Survey](https://mlsanity.com/paper/2405.19334/) (2024)
- [Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning](https://mlsanity.com/paper/2405.18386/) (2024)
- [High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching](https://mlsanity.com/paper/2407.03648/) (2024)
- [Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer](https://mlsanity.com/paper/2409.06096/) (2024)
- [AudioMorphix: Training-free audio editing with diffusion probabilistic models](https://mlsanity.com/paper/2505.16076/) (2025)
- [Steering Autoregressive Music Generation with Recursive Feature Machines](https://mlsanity.com/paper/2510.19127/) (2025)
- [Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models](https://mlsanity.com/paper/2502.07328/) (2025)
- [A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives](https://mlsanity.com/paper/2504.00837/) (2025)
- [GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models](https://mlsanity.com/paper/2506.17886/) (2025)
- [Subtractive Training for Music Stem Insertion using Latent Diffusion Models](https://mlsanity.com/paper/2406.19328/) (2024)
- [Diffusion-based Symbolic Music Generation with Structured State Space Models](https://mlsanity.com/paper/2507.20128/) (2025)
- [The Interpretation Gap in Text-to-Music Generation Models](https://mlsanity.com/paper/2407.10328/) (2024)
- [Pictures Of MIDI: Controlled Music Generation via Graphical Prompts for Image-Based Diffusion Inpainting](https://mlsanity.com/paper/2407.01499/) (2024)
- [EXPOTION: Facial Expression and Motion Control for Multimodal Music Generation](https://mlsanity.com/paper/2507.04955/) (2025)
- [Learning Music Style for Piano Arrangement Through Cross-Modal Bootstrapping](https://mlsanity.com/paper/2608.03050/) (2026)

## Abstract (arXiv)

Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. This approach enables autoregressive language models to seamlessly address music inpainting tasks. Additionally, our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement. We apply this method to fine-tune MusicGen, a leading autoregressive music generation model. Our experiments demonstrate promising results across multiple music editing tasks, offering more flexible controls for future AI-driven music editing tools. The source codes and a demo page showcasing our work are available at https://kikyo-16.github.io/AIR.