论文标题
基于独立矢量提取的联合声音回声取消和盲源提取
Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction
论文作者
论文摘要
我们描述了多微晶声原始前端的联合声音回声取消（AEC）和盲源提取（BSE）方法。提出的算法通过最大化非高斯感兴趣来源的统计独立性以及固定的高斯背景建模来干扰信号和残留回声，从而盲目估算了AEC和波束成形过滤器。双重聊天和快速交配的参数更新来自全局最大样品目标函数，导致计算高效的牛顿型更新规则。与模拟声数据的评估相比，与单独更新两个过滤器相比，拟议联合AEC和光束成型过滤器估计的好处。
We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AEC and beamforming filters by maximizing the statistical independence of a non-Gaussian source of interest and a stationary Gaussian background modeling interfering signals and residual echo. Double talk-robust and fast-converging parameter updates are derived from a global maximum-likelihood objective function resulting in a computationally efficient Newton-type update rule. Evaluation with simulated acoustic data confirms the benefit of the proposed joint AEC and beamforming filter estimation in comparison to updating both filters individually.
