  This paper presents DiffMoog - a differentiable modular synthesizer with a
comprehensive set of modules typically found in commercial instruments. Being
differentiable, it allows integration into neural networks, enabling automated
sound matching, to replicate a given audio input. Notably, DiffMoog facilitates
modulation capabilities (FM/AM), low-frequency oscillators (LFOs), filters,
envelope shapers, and the ability for users to create custom signal chains. We
introduce an open-source platform that comprises DiffMoog and an end-to-end
sound matching framework. This framework utilizes a novel signal-chain loss and
an encoder network that self-programs its outputs to predict DiffMoogs
parameters based on the user-defined modular architecture. Moreover, we provide
insights and lessons learned towards sound matching using differentiable
synthesis. Combining robust sound capabilities with a holistic platform,
DiffMoog stands as a premier asset for expediting research in audio synthesis
and machine learning.
