This paper proposes an improved method of solving the permutation problem inherent in frequency-domain of convolutive blind source separation (BSS). It combines a novel inter-frequency dependence measure: the power ratio of separated signals, and a simple but effective bin-wise permutation alignment scheme. The proposed method is easy to implement and surpasses the conventional ones. Simulations have shown that it can provide an almost ideal solution of the permutation problem for a case where two or three sources were mixed in a room with a reverberation time of 130 ms.

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.