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A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor by Zhou, Yi; Chen, Yufan; Ma, Yongbao; Liu, Hongqing is a Engineering article available to read on EtoBox.

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The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech

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Author

Zhou, Yi; Chen, Yufan; Ma, Yongbao; Liu, Hongqing

Publisher

Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2001-; Publons (ISSN 1424-8220)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)