Abstract: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 set… Show more
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