基于阵元接收信号幅度信息的频域盲分离排序算法

排序模糊性问题是影响频域盲源分离算法分离性能的主要原因之一。对于提出了一种新的解决频域盲源分离排序模糊性问题的算法。该算法通过提取阵元接收信号每个频率点上的幅度衰减信息，采用k—means聚类算法将线性分离算法所得分离信号进行归类，来解决排序模糊性问题。该排序算法对阵列阵元排布方式，阵元间距等没有特殊的要求，并且适用于任意数量混合信号的盲分离系统。仿真实验证实了这种开发阵元接收信号幅度衰减信息的排序算法在绝大多数频率点上有效地解决了排序模糊性问题，是一种计算量相对较小而又简单有效的排序算法。

An algorithm for solving permutation problem based on the magnitude information received by microphones in the frequency-domain BSS

Since the permutation ambiguity is a major problem in the frequency-domain blind source separation, this paper presents a new method for solving this permutation ambiguity problem. The new method extracts information from the magnitude attenuation of the signal received by the microphones and performs k-means clustering in order to assign the separated speech signals to different sources. This new approach does not require any specific arrangement or any limitation to the shape of microphone array and works with any number of mixed signals. Experimental results show that the new approach which exploits the magnitude information received by the microphones is able to provide a reliable permutation alignment in most frequency bins simply and effectively.