A method, system, terminal device, and medium for dual-target speech enhancement in smart glasses based on a multi-microphone array.

By combining a multi-microphone array and a deep neural network, the problems of low power consumption, high real-time performance, and robustness of voice enhancement on the smart glasses end-user side are solved. This enables synchronous enhancement of voice between the wearer and the target voice in a preset direction, as well as multi-task collaboration, thereby improving the efficiency and effectiveness of voice processing.

CN121938388BActive Publication Date: 2026-06-30ELEVOC TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ELEVOC TECH CO LTD
Filing Date
2026-03-30
Publication Date
2026-06-30

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Abstract

This invention discloses a method, system, terminal device, and medium for dual-target speech enhancement in smart glasses based on a multi-microphone array, relating to the field of speech enhancement technology. The method is applied to smart glasses and includes: acquiring noisy multi-channel raw time-domain speech signals through a multi-microphone array of the smart glasses, obtaining a spectrum set through time-frequency domain transformation; extracting the amplitude and phase features of the complex spectrum of each channel in the frequency domain and introducing local temporal context to construct multi-channel spatial acoustic features with decoupled amplitude and phase; modeling the dual-region complex spectrum using a pre-trained deep neural network to obtain the target complex spectrum, and reconstructing it through inverse time-frequency domain transformation to obtain the enhanced independent wearer speech signal and the target speech signal in a preset direction. This invention achieves parallel dual-target speech enhancement and non-target interference suppression, with low power consumption, high real-time performance, adaptability to the stringent performance constraints of smart glasses, and excellent robustness of speech enhancement in complex noise scenarios.
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