| Abstract: |
The wide-sense stationary assumption has been frequently employed in array processing, since it results in uncorrelated frequency bins and consequently major simplifications arise. However,
unlike stationary signals, significant interfrequency correlations are observable in nonstationary signals like speech. Here, we drop the stationarity assumption and will show that taking
interfrequency correlations into account leads to higher noise reduction. We develop a framework to design nonstationary beamformers similar to the stationary Frost beamformers. Based on the
noise model, it can be used to design both fixed and adaptive beamformers. The nonstationary beamformer derived here is a set of time-varying filters and hence can be seen as a set of
time-frequency or wavelet transform filters. |
