Efficient Inverse Modeling of Perceptual Sound Matching with Differentiable Synthesizers

The core message of this article is to propose a novel "perceptual-neural-physical" (PNP) loss function that can efficiently optimize a neural network to retrieve the input parameters of a differentiable synthesizer in order to best imitate a target audio signal, while preserving perceptual fidelity.