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Noise suppression models running in production environments are commonly trained on publicly available datasets. However, this approach leads to regressions in production environments 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 privacypreserving manner. In this paper, we present Aura, a solution to make existing noise suppression test sets more challenging and diverse while limiting the sampling budget. 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 a current benchmark test set in noise suppression by sampling audio files from a new batch of data of 20K clean speech clips from Librivox mixed with noise clips obtained from AudioSet. Aura makes the existing benchmark test set harder by 100% in DNSMOS P.835, a 26% improvement in Spearman's rank correlation coefficient (SRCC) compared to random sampling and, identifies 73% out-of-distribution samples to augment the test set.