Digital audio effects are widely used by audio engineers to alter the acoustic and temporal qualities of audio data. However, these effects can have a large number of parameters which can make them difficult to learn for beginners and hamper creativity for professionals. Recently, there have been a number of efforts to employ progress in deep learning to acquire the low-level parameter configurations of audio effects by minimising an objective function between an input and reference track, commonly referred to as style transfer. However, current approaches use inflexible black-box techniques or require that the effects under consideration are implemented in an auto-differentiation framework. In this work, we propose a deep learning approach to audio production style matching which can be used with effects implemented in some of the most widely used frameworks, requiring only that the parameters under consideration have a continuous domain. Further, our method includes style matching for various classes of effects, many of which are difficult or impossible to be approximated closely using differentiable functions. We show that our audio embedding approach creates logical encodings of timbral information, which can be used for a number of downstream tasks. Further, we perform a listening test which demonstrates that our approach is able to convincingly style match a multi-band compressor effect.

翻译：数字音频效果被音频工程师广泛用于改变音频数据的声学和时间特性。然而，这些效果通常包含大量参数，导致初学者难以掌握，并可能限制专业人士的创造力。近年来，人们尝试利用深度学习进展，通过最小化输入音轨与参考音轨之间的目标函数来获取音频效果的低层级参数配置（通常称为风格迁移）。但现有方法要么采用不灵活的黑箱技术，要么要求所考虑的效果必须在自动微分框架中实现。本研究提出一种面向音频制作风格匹配的深度学习方法，可兼容当前最广泛使用的框架中的效果，仅需保证所考察参数具有连续域。此外，我们的方法能够对多种类型的效果进行风格匹配——其中许多效果难以甚至无法用可微函数近似逼近。实验表明，我们的音频嵌入方法能生成音色信息的逻辑编码，可用于多种下游任务。我们还进行了听力测试，证明该方法能令人信服地完成多频段压缩器效果的风格匹配。