GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework
- Ang Lv (Renmin University of China) email@example.com
- Xu Tan^ (Microsoft Research Asia) firstname.lastname@example.org
- Peiling Lu (Microsoft Research Asia) email@example.com
- Wei Ye (Peking University) firstname.lastname@example.org
- Shikun Zhang (Peking University) email@example.com
- Jiang Bian (Microsoft Research Asia) firstname.lastname@example.org
- Rui Yan^ (Renmin University of China) email@example.com
^ Corresponding author.
Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrumental tracks from scratch, or based on user-provided source tracks. Considering the diverse and flexible combination between source and target tracks, a unified model capable of generating any arbitrary tracks is of crucial necessity. Previous works fail to address this need due to inherent constraints in music representations and model architectures. To address this need, we propose a unified representation and diffusion framework named GETMusic (‘GET’ stands for GEnerate music Tracks), which includes a novel music representation named GETScore, and a diffusion model named GETDiff. GETScore represents notes as tokens and organizes them in a 2D structure, with tracks stacked vertically and progressing horizontally over time. During training, tracks are randomly selected as either the target or source. In the forward process, target tracks are corrupted by masking their tokens, while source tracks remain as ground truth. In the denoising process, GETDiff learns to predict the masked target tokens, conditioning on the source tracks. With separate tracks in GETScore and the non-autoregressive behavior of the model, GETMusic can explicitly control the generation of any target tracks from scratch or conditioning on source tracks. We conduct experiments on music generation involving six instrumental tracks, resulting in a total of 665 combinations. GETMusic provides high-quality results across diverse combinations and surpasses prior works proposed for some specific combinations.
Diverse Composition Tasks Showcasing
1. Lead to Bass, Drum, Guitar, Piano, and String
2. Piano to Drum, Guitar, and String
3. Guitar to String
4. Lead to Piano, String
5. Drum and Piano to Lead and String
6. 6-Track Music Generation from Scratch 1
7. 6-Track Music Generation from Scratch 2
8. Infilling + Track-wise 1
9. Infilling + Track-wise 2
10. Bach Style Generation (Finetune on JSB Chorales dataset)
Thank you for watching 💗!