NEC: Speaker Selective Cancellation via Neural Enhanced Ultrasound Shadowing

TL;DR

NEC introduces a neural-enhanced ultrasound shadowing technique that selectively cancels a target speaker's voice in real-time, preventing unauthorized microphone capture without affecting other conversations.

Contribution

The paper presents a novel neural network-based method that modulates ultrasound to selectively cancel a target speaker's voice, improving privacy and security in audio recordings.

Findings

Effective real-time voice cancellation demonstrated on smartphones.

Selective cancellation without interfering with other speakers.

Utilizes ultrasound modulation and microphone non-linearity for accuracy.

Abstract

In this paper, we propose NEC (Neural Enhanced Cancellation), a defense mechanism, which prevents unauthorized microphones from capturing a target speaker's voice. Compared with the existing scrambling-based audio cancellation approaches, NEC can selectively remove a target speaker's voice from a mixed speech without causing interference to others. Specifically, for a target speaker, we design a Deep Neural Network (DNN) model to extract high-level speaker-specific but utterance-independent vocal features from his/her reference audios. When the microphone is recording, the DNN generates a shadow sound to cancel the target voice in real-time. Moreover, we modulate the audible shadow sound onto an ultrasound frequency, making it inaudible for humans. By leveraging the non-linearity of the microphone circuit, the microphone can accurately decode the shadow sound for target voice cancellation. We implement and evaluate NEC comprehensively with 8 smartphone microphones in different settings. The results show that NEC effectively mutes the target speaker at a microphone without interfering with other users' normal conversations.