宽带
计算机科学
宽带音频
Echo(通信协议)
语音识别
人工神经网络
融合
深度学习
人工智能
电子工程
工程类
语音编码
计算机网络
音频信号
哲学
数字音频
语言学
作者
Haoran Zhao,Nan Li,Runqiang Han,Lianwu Chen,Xiguang Zheng,Chen Zhang,Liang Guo,Bing Yu
标识
DOI:10.1109/icassp43922.2022.9746272
摘要
Deep learning based wideband (16kHz) acoustic echo cancellation (AEC) approaches have surpassed traditional methods. This work proposes a deep hierarchical fusion (DHF) network with intra-network and inter-network fusion to further improve the wideband AEC performance. Meanwhile, this work extends the existing wideband systems to enable fullband (48kHz) AEC while simultaneously ensuring automatic speech recognition compatibility by incorporating with an ASR loss. The proposed system has ranked 2nd place in ICASSP 2022’s AEC Challenge.
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