Neural audio synthesis methods now allow specifying ideas in natural
language. However, these methods produce results that cannot be easily tweaked,
as they are based on large latent spaces and up to billions of uninterpretable
parameters. We propose a text-to-audio generation method that leverages a
virtual modular sound synthesizer with only 78 parameters. Synthesizers have
long been used by skilled sound designers for media like music and film due to
their flexibility and intuitive controls. Our method, CTAG, iteratively updates
a synthesizer's parameters to produce high-quality audio renderings of text
prompts that can be easily inspected and tweaked. Sounds produced this way are
also more abstract, capturing essential conceptual features over fine-grained
acoustic details, akin to how simple sketches can vividly convey visual
concepts. Our results show how CTAG produces sounds that are distinctive,
perceived as artistic, and yet similarly identifiable to recent neural audio
synthesis models, positioning it as a valuable and complementary tool.

我们提出了一种文本到音频生成方法（CTAG），该方法利用具有仅有 78 个参数的虚拟模块化声音合成器，通过迭代更新合成器的参数，生成易于检查和调整的高质量音频渲染，其中产生的声音更抽象，捕捉了基本概念特征而非细粒度声学细节，类似于简单的素描可以生动地传达视觉概念。我们的研究结果显示 CTAG 生成的声音独特，并被视为艺术性的，同时也可以类似地识别为最近的神经音频合成模型，使其成为一种有价值且互补的工具。