In this paper, we propose a robust room inverse filtering algorithm for speech dereverberation based on a kurtosis maximization. The proposed algorithm utilizes a new normalized kurtosis function that nonlinearly maps the input kurtosis onto a finite range from zero to one, which results in a kurtosis warping. Due to the kurtosis warping, the proposed algorithm provides more stable convergence and, in turn, better performance than the conventional algorithm. Experimental results are presented to confirm the robustness of the proposed algorithm.

Several methods have been developed for solving blind deconvolution problem. Recursive inverse filtering method is proposed recently and shown to have good convergence properties. This method requires accurate estimate of the region of support. In this paper, we propose to modify the original method by incorporating split, merge and grouping algorithm to find the region of support automatically.

A new method is proposed for recovering an unknown source signal ,which is observed through two unknown channels characterized by non-minimum phase FIR filters. Conventional methods cannot estimate the non-minimum phase parts and recover the source signal. Our method is based on computing the eigenvector corresponding to the smallest eigenvalue of the input correlation matrix and using the criterion with the multi-channnel inverse filtering theory. The impulse responses are estimated by computing the eigenvector for all modeling orders. The optimum order is searched for using the criterion and the most appropriate impulse responses are estimated. Multi-channel inverse filtering with the estimated impulse responses is used to recover the unknown source signal. Computer simulation shows that our method can estimate nonminimum phase impulse responses from two reverberant signals and recover the source signal.

A novel method of multichannel surface EMG processing has been developed to compensate for the distortion in bipolar surface EMG signals due to the movement of innervation zones. The distortion of bipolar surface EMG signals was mathematically described as a filtering function. A compensating technique for such distorted bipolar surface EMG signals was developed for the brachial biceps during dynamic contractions in which the muscle length and tension change. The technique is based on multichannel surface EMG measurement, a method for estimating the movement of an innervation zone, and the inverse filtering technique. As a result, the distorted EMG signals were compensated and transformed into nearly identical waveforms, independent of the movement of the innervation zone.