Generating multi-instrument music from symbolic music representations is an
important task in Music Information Retrieval (MIR). A central but still
largely unsolved problem in this context is musically and acoustically informed
control in the generation process. As the main contribution of this work, we
propose enhancing control of multi-instrument synthesis by conditioning a
generative model on a specific performance and recording environment, thus
allowing for better guidance of timbre and style. Building on state-of-the-art
diffusion-based music generative models, we introduce performance conditioning
- a simple tool indicating the generative model to synthesize music with style
and timbre of specific instruments taken from specific performances. Our
prototype is evaluated using uncurated performances with diverse
instrumentation and achieves state-of-the-art FAD realism scores while allowing
novel timbre and style control. Our project page, including samples and
demonstrations, is available at benadar293.github.io/midipm

在音乐信息检索（MIR）中，从符号音乐表示生成多仪器音乐是一项重要任务。本研究的主要贡献是提出通过将生成模型的条件设置为特定表演和录音环境，从而增强多仪器合成的控制能力，从而更好地引导音色和风格。通过基于最先进的扩散音乐生成模型，我们引入了性能条件化，这是一种简单的工具，表明生成模型可以合成具有特定表演所使用的特定乐器的音乐的风格和音色。我们的原型采用各种仪器的非编目表演进行评估，实现了最先进的 FAD 真实性评分，并允许新的音色和风格控制。我们的项目页面，包括样本和演示，可在 benadar293.github.io/midipm 上找到。