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Title: Dual-Path Minimum-Phase and All-Pass Decomposition Network for Single Channel Speech Dereverberation
With the development of deep neural networks (DNN), many DNN-based speech dereverberation approaches have been proposed to achieve significant improvement over the traditional methods. However, most deep learning-based dereverberation methods solely focus on suppressing time-frequency domain reverberations without utilizing cepstral domain features which are potentially useful for dereverberation. In this paper, we propose a dual-path neural network structure to separately process minimum-phase and all-pass components of single channel speech. First, we decompose speech signal into minimum-phase and all-pass components in cepstral domain, then Conformer embedded U-Net is used to remove reverberations of both components. Finally, we combine these two processed components together to synthesize the enhanced output. The performance of proposed method is tested on REVERB-Challenge evaluation dataset in terms of commonly used objective metrics. Experimental results demonstrate that our method outperforms other compared methods.  more » « less
Award ID(s):
2016725
PAR ID:
10542795
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-4485-1
Page Range / eLocation ID:
1261 to 1265
Subject(s) / Keyword(s):
Fearless Steps APOLLO Speech Dereverberation Minimim Phase U-Net REVERB Challenge DNN
Format(s):
Medium: X
Location:
Seoul, Korea, Republic of
Sponsoring Org:
National Science Foundation
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