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.

本研究提出了将源分离和对抗表示学习两种常用方法结合在隐私保护中的智能声学监测系统中，通过学习音频记录的潜在表示，实现区分语音和非语音记录，从而有效提高语音隐私保护，同时在声学监测任务中保持良好的性能。