  Speech dereverberation aims to alleviate the negative impact of late
reverberant reflections. 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. 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.
