提出单参数盲波束成形法，提升语音分离精度。
Blind Capon Beamformer Based on Independent Component Extraction: Single-Parameter Algorithm,
- 基于独立分量提取，仅优化一个角度参数。
- 在低混响环境下实现更高语音提取准确率。
- 适合需要高精度声源分离的场景。
针对线性传感器阵列中的相位偏移混合模型，本文提出一种盲Capon波束成形方法，旨在寻找输出与其他信号独立的方向。该算法基于独立分量提取，并引入正交约束，从而只需优化一个与到达角相关的实数参数。推导了均干扰-信号比的Cramér-Rao下界。算法与传统盲方法及到达角估计+波束成形方法对比，显示在提取精度上有显著提升。在低混响房间中进行了频域说话人分离的应用验证。
原文摘要 · Abstract (English)
We consider a phase-shift mixing model for linear sensor arrays in the context of blind source extraction. We derive a blind Capon beamformer that seeks the direction where the output is independent of the other signals in the mixture. The algorithm is based on Independent Component Extraction and imposes an orthogonal constraint, thanks to which it optimizes only one real-valued parameter related to the angle of arrival. The Cramér-Rao lower bound for the mean interference-to-signal ratio is derived. The algorithm and the bound are compared with conventional blind and direction-of-arrival estimation+beamforming methods, showing improvements in terms of extraction accuracy. An application is demonstrated in frequency-domain speaker extraction in a low-reverberation room.
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