.png){kind=icon}
返回
Distributed speech dereverberation using weighted prediction error
.png){kind=icon}
OA
AI
DOI：10.1016/j.sigpro.2024.109577.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Speech dereverberation aims to alleviate the negative impact of late reverberant components. The weighted prediction error (WPE) method is a well-established technique known for its superior performance in dereverberation. However, in scenarios where microphone nodes are dispersed, the centralized approach of the WPE method requires aggregating all observations for inverse filtering, resulting in a significant computational burden in a single fusion center. This paper introduces a distributed speech dereverberation method that emphasizes low computational complexity at each node. Specifically, we leverage the distributed adaptive node-specific signal estimation (DANSE) algorithm within the multichannel linear prediction (MCLP) process. This approach empowers each node to perform local operations with reduced complexity while achieving the global performance through inter-node cooperation. Experimental results validate the effectiveness of our proposed method, showcasing its ability to achieve efficient speech dereverberation in dispersed microphone node scenarios.
Keyword:
Speech dereverberation
Distributed estimation
The weighted prediction error method
Far-field scenario
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
A Simple Theory and New Method of Differential Beamforming With Uniform Linear Microphone Arrays
.png){kind=icon}
PREAI
分享收藏
Livestock intensification and environmental sustainability: An analysis based on pasture management scenarios in the brazilian savanna畜牧业集约化与环境可持续性：基于巴西稀树草原牧场管理情景的分析
PREAI
分享收藏
查看更多内容