Aura: Privacy-preserving augmentation to improve test set diversity in noise suppression applications. (arXiv:2110.04391v2 [eess.AS] UPDATED)

Noise suppression models running in production environments are commonly
trained on publicly available datasets. However, this approach leads to
regressions due to the lack of training/testing on representative customer
data. Moreover, due to privacy reasons, developers cannot listen to customer
content. This `ears-off' situation motivates augmenting existing datasets in a
privacy-preserving manner. In this paper, we present \aura, a solution to make
existing noise suppression test sets more challenging and diverse while being
sample efficient. \aura is `ears-off' because it …

applications aura diversity privacy test