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.

我们提出了一种深度学习方法用于音频制作风格匹配，可以与大多数常用框架中实现的效果一起使用，只要考虑的参数在连续范围内，能够通过逻辑编码音色信息来进行风格匹配。

非可微音频效果的样式转移

Style Transfer for Non-differentiable Audio Effects

Learning to program an audio production VST synthesizer is a time consuming
process, usually obtained through inefficient trial and error and only mastered
after years of experience. As an educational and creative tool for sound
designers, we propose SerumRNN: a system that provides step-by-step
instructions for applying audio effects to change a user's input audio towards
a desired sound. We apply our system to Xfer Records Serum: currently one of
the most popular and complex VST synthesizers used by the audio production
community. Our results indicate that SerumRNN is consistently able to provide
useful feedback for a variety of different audio effects and synthesizer
presets. We demonstrate the benefits of using an iterative system and show that
SerumRNN learns to prioritize effects and can discover more efficient effect
order sequences than a variety of baselines.

提出了一种名为 SerumRNN 的系统，该系统可以为音乐制作人提供逐步应用音频效果以实现期望声音的指导，从而节省了学习 VST 合成器的时间，同时也能够学习优先考虑的效果和发现更高效的效果排序序列。