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MULTILAYER ADAPTATION BASED COMPLEX ECHO CANCELLATION AND VOICE ENHANCEMENT
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP2018_IDSP-P1.3_SBNR_SBRES_NLEC.png?itok=ysljD06D)
- Citation Author(s):
- Submitted by:
- Jun Yang
- Last updated:
- 7 May 2019 - 5:08pm
- Document Type:
- Poster
- Document Year:
- 2018
- Event:
- Presenters:
- Jun Yang
- Paper Code:
- IDSP-P1.3
- Categories:
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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