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Learning Ratio Mask with Cascaded Deep Neural Networks for Echo Cancellation in Laser Monitoring Signals

School of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
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Electronics 2020, 9(5), 856; https://doi.org/10.3390/electronics9050856
Received: 23 April 2020 / Revised: 8 May 2020 / Accepted: 15 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Theory and Applications in Digital Signal Processing)
Laser monitoring has received more and more attention in many application fields thanks to its essential advantages. The analysis shows that the target speech in the laser monitoring signals is often interfered by the echoes, resulting in a decline in speech intelligibility and quality, which in turn affects the identification of useful information. The cancellation of echoes in laser monitoring signals is not a trivial task. In this article, we formulate it as a simple but effective additive echo noise model and propose a cascade deep neural networks (C-DNNs) as the mapping function from the acoustic feature of noisy speech to the ratio mask of clean signal. To validate the feasibility and effectiveness of the proposed method, we investigated the effect of echo intensity, echo delay, and training target on the performance. We also compared the proposed C-DNNs to some traditional and newly emerging DNN-based supervised learning methods. Extensive experiments demonstrated the proposed method can greatly improve the speech intelligibility and speech quality of the echo-cancelled signals and outperform the comparison methods. View Full-Text
Keywords: echo cancellation; speech enhancement; speech separation; deep neural networks (DNNs); cascaded DNNs (C-DNNs); laser monitoring echo cancellation; speech enhancement; speech separation; deep neural networks (DNNs); cascaded DNNs (C-DNNs); laser monitoring
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Lang, H.; Yang, J. Learning Ratio Mask with Cascaded Deep Neural Networks for Echo Cancellation in Laser Monitoring Signals. Electronics 2020, 9, 856.

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