Multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Possible applications for this technology include audio surveillance, identity verification, video chatting, and conference calling and sound source localization. Features a theoretical study and experimental validation on a binaural hearing aid setup of this standard SDW-MWF implementation, where the effect of estimation errors in the second order statistics is analyzed. In this case, and for a single target speech source, the standard SDW-MWF implementation is found not to behave as predicted theoretically. Second, two recently introduced alternative filters, namely the rank-one SDW-MWF and the spatial prediction SDW-MWF, are also studied in the presence of estimation errors in the second order statistics. These filters implicitly assume a single target speech source, but still only rely on the speech and noise correlation matrices. In the proposed system, the resultant of wiener filter is given to the Butterworth filter in order to increase the signal-to-noise ratio. The Butterworth filter is a type of signal processing filter designed to have as flat a frequency response as possible in the passband. It is also referred to as a maximally flat magnitude filter. Such an ideal filter cannot be achieved but Butterworth showed that successively closer approximations were obtained with increasing numbers of filter elements of the right values. At the time, filters generated substantial ripple in the passband, and the choice of component values was highly interactive. Butterworth showed that a low pass filter could be designed whose cutoff frequency was normalized to 1 radian per second and whose frequency response.