计算机科学
混响
计算复杂性理论
波束赋形
还原(数学)
算法
卡尔曼滤波器
滤波器(信号处理)
卷积码
噪音(视频)
接头(建筑物)
卷积神经网络
自适应滤波器
语音识别
人工智能
数学
计算机视觉
电信
解码方法
声学
工程类
建筑工程
物理
几何学
图像(数学)
作者
Sebastian Braun,Ivan Tashev
标识
DOI:10.1109/waspaa52581.2021.9632780
摘要
Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks. While longer filters are required to model long reverberation times, the computational burden of current online solutions grows fast with the filter length and number of microphones. In this work, we propose a low complexity convolutional beamformer using a Kalman filter derived affine projection algorithm to solve the adaptive filtering problem. The proposed solution is several orders of magnitude less complex than comparable existing solutions while slightly outperforming them on the REVERB challenge dataset.
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