Adversarial Representation Learning for Robust Privacy Preservation in Audio. (arXiv:2305.00011v1 [cs.SD])

Sound event detection systems are widely used in various applications such as
surveillance and environmental monitoring where data is automatically
collected, processed, and sent to a cloud for sound recognition. However, this
process may inadvertently reveal sensitive information about users or their
surroundings, hence raising privacy concerns. In this study, we propose a novel
adversarial training method for learning representations of audio recordings
that effectively prevents the detection of speech activity from the latent
features of the recordings. The proposed …

adversarial applications audio cloud data detection environmental event information may monitoring preservation privacy process recognition representation sensitive information sound surveillance systems