2021
DOI: 10.48550/arxiv.2110.04391
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 14 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?