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MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT

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Citation Author(s):
Submitted by:
Jun Yang
Last updated:
7 May 2019 - 5:08pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Jun Yang
Paper Code:
IDSP-P1.3

Abstract 

Abstract: 

The paper proposes an efficient signal processing system mainly consisting of an adaptation-based nonlinear echo cancellation (NLEC) layer and a joint perceptual subband residual echo suppression (SBRES) layer and noise reduction (SBNR) layer. The theoretical analyses, subjective and objective test results show that the proposed signal processing system can offer a significant improvement for automatic speech recognition and full-duplex voice communication performance in emerging artificial intelligence speakers. The proposed SBRES and NLEC layers can reduce various types of echoes including linear, nonlinear, and time-variant echo. Correspondingly, the proposed SBNR layer can effectively reduce not only noises but also echoes that have the similar statistical characteristics to noises. Non-uniform auditory perceptual critical bands are employed so as to better reflect cochlea mechanisms. The SBRES and SBNR layers are jointly accomplished in frequency domain, which results in a significant reduction of MIPS consumption from real time implementation point of view.

Full-length paper: https://ieeexplore.ieee.org/document/8461354

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JunYang_ICASSP2018_v3.pdf

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