Multichannel Wiener filtering via multichannel decorrelation

Extracting a target source signal from multiple noisy observations is an essential task in many applications of signal processing such as digital communications or speech and audio processing. The multi-channel Wiener filter is able to solve this task in a minimum-mean-square-error (MMSE) optimal way by applying a spatial filter succeeded by a spectral postfilter. Its direct implementation, however, is difficult due to requiring the statistics of the unobservable source and noise signals. In this paper, we apply the signal-separation-based technique of multichannel decorrelation and reveal its relation to the Wiener post-filtering component. On this basis, we present a numerically robust and efficient adaptive algorithm to find an estimate of the MMSE-optimal postfilter based on the statistics of the observable signals alone. Experimental evaluation demonstrates the validity of the proposed approach and confirms the convergence of the adaptive algorithm to the MMSE-optimal postfilter solution.