Differentiable programming via automatic differentiation (AD) is the foundation for gradient-based optimisation techniques, and forms the basis for many current approaches
to machine learning. Though well catered-for in general-purpose programming languages, the availability of AD in a domain-specific language (DSL) could offer novel perspectives on optimisation problems in the field of audio. We present a general scheme for differentiable programming in the Faust programming language, a high-performance DSL tailored to audio synthesis and signal processing. Faust`s rich ecosystem, coupled with a comprehensive AD implementation, can provide support for audio optimisation and machine learning applications on a multitude of platforms, from FPGAs to the web.
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https://aes2.org/publications/elibrary-page/?id=23017
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