  The term "differentiable digital signal processing" describes a family of
techniques in which loss function gradients are backpropagated through digital
signal processors, facilitating their integration into neural networks. This
article surveys the literature on differentiable audio signal processing,
focusing on its use in music & speech synthesis. We catalogue applications to
tasks including music performance rendering, sound matching, and voice
transformation, discussing the motivations for and implications of the use of
this methodology. This is accompanied by an overview of digital signal
processing operations that have been implemented differentiably. Finally, we
highlight open challenges, including optimisation pathologies, robustness to
real-world conditions, and design trade-offs, and discuss directions for future
research.
