| Summary: | 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.
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