MicAugment: One-shot Microphone Style Transfer

A critical aspect for the successful deployment of audio-based models ``in-the-wild'' is the robustness to the transformations introduced by heterogeneous microphones. In this work we propose a method that is able to perform \emph{one-shot microphone style} transfer. Given only a \emph{few seconds} of audio recorded by a target device, \emph{MicAugment} identifies the transformations associated to the microphone and uses the learned transformations to synthesize audio as if it were recorded by that device. We show that our method can successfully apply the style of a target microphone and that it significantly increases model robustness to microphone variability when used as \emph{data augmentation} in downstream tasks.

• Machine Intelligence

• Speech Processing