Abstract
Acoustic sensing offers a promising non-intrusive approach for monitoring daily activities of older adults, yet speech privacy concerns remain a critical barrier to real-world deployment. We present a privacy firewall pipeline based on a U-Net encoder-decoder, trained entirely on synthetic data, that removes speech from ambient audio while preserving environmental sounds indicative of daily activities. Activity recognition is performed using VGGish transfer learning with an SVM classifier. Evaluated on the ESC-50 and SINS datasets across multiple speech content levels, the proposed model reduced residual speech to 0% VAD-detectable speech (Silero Voice Activity Detection) under all tested conditions, outperforming Facebook Denoiser (6.55% residual), SepFormer (36.34%) and ConvTasNet (47.21%) on ESC-50 at the 100\% speech level. On ESC-50 at 40% speech level, classification performance recovers to 85% precision and 85% recall after speech removal, compared with 81%/75% before removal and an 84%/83% speech-free baseline. Evaluation on real-world participant home recordings collected with the AudioHive app showed 0% VAD-detectable speech after processing while maintaining 76% precision and recall. The pipeline enables privacy-preserving acoustic sensing without sacrificing activity recognition performance, addressing a key obstacle to the adoption of ambient monitoring in elderly care.
Abstract (translated)
URL
https://arxiv.org/abs/2609.02376
