Representation Learning for Audio Privacy Preservation using Source
  Separation and Robust Adversarial Learning
Diep Luong Minh Tran Shayan Gharib Konstantinos Drossos Tuomas Virtanen
Privacy preservation has long been a concern in smart acoustic monitoring
systems, where speech can be passively recorded along with a target signal in
the system's operating environment. In this study, we propose the integration
of two commonly used approaches in privacy preservation: source separation and
adversarial representation learning. The proposed system learns the latent
representation of audio recordings such that it prevents differentiating
between speech and non-speech recordings. Initially, the source separation
network filters out some of the privacy-sensitive data, and during the
adversarial learning process, the system will learn privacy-preserving
representation on the filtered signal. We demonstrate the effectiveness of our
proposed method by comparing our method against systems without source
separation, without adversarial learning, and without both. Overall, our
results suggest that the proposed system can significantly improve speech
privacy preservation compared to that of using source separation or adversarial
learning solely while maintaining good performance in the acoustic monitoring
task.