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.

翻译：神经音频合成方法现已允许通过自然语言描述创意。然而，这些方法生成的结果难以微调，因为它们依赖于大型潜在空间和高达数十亿个不可解释的参数。我们提出了一种文本到音频生成方法，该方法利用仅含78个参数的虚拟模块化声音合成器。合成器因其灵活性和直观的控制，长期以来被专业的音效设计师用于音乐和电影等媒体创作。我们的方法CTAG通过迭代更新合成器的参数，生成与文本提示相匹配的高质量音频渲染结果，这些结果易于检查和调整。以此方式产生的声音也更具抽象性，能够捕捉本质的概念特征而非细粒度的声学细节，类似于简单草图能够生动传达视觉概念的方式。我们的结果表明，CTAG生成的声音具有独特性，被感知为具有艺术性，同时与近期神经音频合成模型的识别度相当，这使其成为一种有价值且具有互补性的工具。