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arXiv · 2207.05848

NEC: Speaker Selective Cancellation via Neural Enhanced Ultrasound Shadowing

Hanqing Guo·Chenning Li·Lingkun Li·Zhichao Cao·Qiben Yan·Li Xiao
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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.

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Hanqing Guo, Chenning Li, Lingkun Li, Zhichao Cao, Qiben Yan, Li Xiao. 2022-07-12. NEC: Speaker Selective Cancellation via Neural Enhanced Ultrasound Shadowing. https://arxiv.org/abs/2207.05848

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