  We present a framework that can impose the audio effects and production style
from one recording to another by example with the goal of simplifying the audio
production process. We train a deep neural network to analyze an input
recording and a style reference recording, and predict the control parameters
of audio effects used to render the output. In contrast to past work, we
integrate audio effects as differentiable operators in our framework, perform
backpropagation through audio effects, and optimize end-to-end using an
audio-domain loss. We use a self-supervised training strategy enabling
automatic control of audio effects without the use of any labeled or paired
training data. We survey a range of existing and new approaches for
differentiable signal processing, showing how each can be integrated into our
framework while discussing their trade-offs. We evaluate our approach on both
speech and music tasks, demonstrating that our approach generalizes both to
unseen recordings and even to sample rates different than those seen during
training. Our approach produces convincing production style transfer results
with the ability to transform input recordings to produced recordings, yielding
audio effect control parameters that enable interpretability and user
interaction.
