Generalized Combined Nonlinear Adaptive Filters for Nonlinear Acoustic Echo Cancellation

Lu, Wenxia; Zhang, Lijun; Chen, Jie; Chen, Jingdong

doi:10.1109/apsipaasc47483.2019.9023285

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…When considering both the environmental noise and the residual echo in practical applications, we need to suppress both the noise and the residual echo. Many algorithms that combine noise and residual echo suppression have already been proposed in the last three decades [6], [7]. In more recent years, due to the powerful capability of deep neural networks (DNNs) in uncovering the complicated nonlinear relation between input and output, DNN based end-to-end AEC algorithms have shown their superior performance compared with unsupervised algorithms [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

ICASSP 2021 Acoustic Echo Cancellation Challenge: Integrated Adaptive Echo Cancellation with Time Alignment and Deep Learning-Based Residual Echo Plus Noise Suppression

Peng

Cheng

Zheng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper describes a three-stage acoustic echo cancellation (AEC) and suppression framework for the ICASSP 2021 AEC Challenge. In the first stage, a partitioned block frequency domain adaptive filtering is implemented to cancel the linear echo components without introducing the near-end speech distortion, where we compensate the time delay between the far-end reference signal and the microphone signal beforehand. In the second stage, a deep complex U-Net integrated with gated recurrent unit is proposed to further suppress the residual echo components. In the last stage, an extremely tiny deep complex U-Net is trained to suppress non-speech residual components that have not been suppressed completely in the second stage, which can also further increase the echo return loss enhancement (ERLE) without increasing the computational complexity dramatically. Experimental results show that the proposed three-stage framework can get the ERLE higher than 50 dB in both single-talk and double-talk scenarios, and perceptual evaluation of speech quality can be improved about 0.75 in double-talk scenarios. The proposed framework outperforms the AEC-Challenge baseline ResRNN by 0.12 points in terms of the MOS.

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