带先验信息的FastICA：半盲最小方差无畸变波束形成器
Informed FastICA: Semi-Blind Minimum Variance Distortionless Beamformer
AI总结：
该研究提出带先验信息的半盲FastICA/FastIVA变体，用MVDR解析形式替换原算法的正交约束，以加权协方差矩阵提供辅助信息，在模型驱动与学习驱动的提取方法间建立关联，经测试收敛快、性能良好。
摘要：
基于非高斯性的独立向量提取在正交约束下采用近似牛顿-拉夫逊算法优化似然函数时，衍生出了著名的单单元FastICA/FastIVA算法。本文用最小方差无畸变波束形成器（MVDR）的解析形式替换上述约束，得到了FastICA/FastIVA的一种半盲变体。此处的辅助信息由替代噪声协方差矩阵的加权协方差矩阵提供，而该加权协方差矩阵的估计正是神经波束形成器的常见目标。该算法由此在基于模型的盲提取与基于学习的提取之间建立了直观联系。算法在仿真实验和说话人ID引导的说话人提取任务中进行了测试，展现出收敛速度快、性能可观的特点。
英文摘要：
Non-Gaussianity-based Independent Vector Extraction leads to the famous one-unit FastICA/FastIVA algorithm when the likelihood function is optimized using an approximate Newton-Raphson algorithm under the orthogonality constraint. In this paper, we replace the constraint with the analytic form of the minimum variance distortionless beamformer (MVDR), by which a semi-blind variant of FastICA/FastIVA is obtained. The side information here is provided by a weighted covariance matrix replacing the noise covariance matrix, the estimation of which is a frequent goal of neural beamformers. The algorithm thus provides an intuitive connection between model-based blind extraction and learning-based extraction. The algorithm is tested in simulations and speaker ID-guided speaker extraction, showing fast convergence and promising performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
