Aura: Privacy-preserving augmentation to improve test set diversity in noise suppression applications

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 relies on a feature extractor and a metric of speech quality, DNSMOS P.835, both pre-trained on data obtained from public sources. As an application of \aura, we augment the INTERSPEECH 2021 DNS challenge by sampling audio files from a new batch of data of 20K clean speech clips from Librivox mixed with noise clips obtained from Audio Set. \aura makes the existing benchmark test set harder by 0.27 in DNSMOS P.835 OVLR (7\%), $0.64$ harder in DNSMOS P.835 SIG (16\%), increases diversity by $31\%$, and achieves a $26\%$ improvement in Spearman's rank correlation coefficient (SRCC) compared to random sampling. Finally, we open-source \aura to stimulate research of test set development.