Spatial Maximum a Posteriori Post-Filtering for Arbitrary Beamforming

We present a new approach for residual transient noise suppression at the output of an arbitrary beamformer. A spatial optimum estimate for the instantaneous a posteriori SNR is derived on the basis of the output signals of a blocking matrix. The optimization problem is formulated in the logarithmic domain and statistical models for the obtained quantities are given. Based on these models the optimization problem is solved in the maximum a posteriori sense. It is shown that the performance of speech recognition systems in non-stationary noise scenarios is improved considerably compared to the performance achieved with a Wiener filter applied to the beamformer output.