This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity nonparametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.

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This paper discusses a multirate filterbank-based extended infomax algorithm for real-world signal separation, i.e., convolved mixtures separation. Since convolution in the time domain corresponds to instantaneous mixing in the frequency domain, polyphase subband projection naturally becomes an efficient alternative to the Fourier transform based frequency domain approach. The online implementation proposed is featured by a simultaneous inverse channel identification in the frequency domain and signal filtering in the time domain. It is shown that an over-representation structure reduces aliasing between different bands and results in more accurate inverse channel estimates. Therefore, it provides better performance than the Fourier transform based structure in the measures of both separation and distortion. The performance limitation of the method is also evaluated in terms of the Wiener solution.Index Terms-Blind system identification, microphone array, polyphase subband filterbank, signal separation, speech enhancement.

This paper presents a method for blind beamforming with application in realtime speech extraction in a non-stationary environment. The blind beamforming is carried out using an online kurtosis maximization approach where the optimization is based on Newton's method. The main novelty of the paper lies in the formulation of the subband kurtosis approximation, where a locally quadratic criterion is solved at each iteration. Further, a real-time digital signal processor (DSP) implementation of the method is conducted and results with real data is presented.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. Subband processing introduces transmission delays caused by the filter bank and signal degradations due to aliasing effects. One efficient way to reduce the aliasing effects is to allow a higher sample rate than critically needed in the subbands and thus reduce subband signal degradation. We suggest a design method, for uniform DFT filter banks with any oversampling factor, where the total filter bank group delay may be specified, and where the aliasing and magnitude/phase distortions are minimized.