Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrument tracks based on provided source tracks. In practical scenarios where there's a predefined ensemble of tracks and various composition needs, an efficient and effective generative model that can generate any target tracks based on the other tracks becomes crucial. However, previous efforts have fallen short in addressing this necessity due to limitations in their music representations and models. In this paper, we introduce a framework known as GETMusic, with ``GET'' standing for ``GEnerate music Tracks.'' This framework encompasses a novel music representation ``GETScore'' and a diffusion model ``GETDiff.'' GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time. At a training step, each track of a music piece is randomly selected as either the target or source. The training involves two processes: In the forward process, target tracks are corrupted by masking their tokens, while source tracks remain as the ground truth; in the denoising process, GETDiff is trained to predict the masked target tokens conditioning on the source tracks. Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations. Our experiments demonstrate that the versatile GETMusic outperforms prior works proposed for certain specific composition tasks.

翻译：符号音乐生成旨在创建音符序列，帮助用户进行音乐创作，例如基于提供的源轨道生成目标乐器轨道。在预设轨道编排与多样化作曲需求的现实场景中，能够基于其他轨道生成任意目标轨道的高效生成模型至关重要。然而，由于音乐表示方法与模型设计的局限性，现有研究未能充分满足这一需求。本文提出GETMusic框架（“GET”指代“生成音乐轨道”），包含新型音乐表示GETScore与扩散模型GETDiff。GETScore将音符表示为标记，并以二维结构组织——轨道纵向堆叠，时间维度横向展开。训练步骤中，乐曲的每条轨道被随机选择为目标轨道或源轨道。训练包含两个过程：正向过程中，目标轨道通过掩码标记被破坏，源轨道保持真实值；去噪过程中，GETDiff学习在源轨道条件下预测被掩码的目标标记。我们所提出的表示方法结合非自回归生成模型，使GETMusic能够基于任意源-目标轨道组合生成音乐。实验表明，多功能的GETMusic在多项特定作曲任务上优于先前提出的专用模型。