Towards robust acoustic echo cancellation during double-talk and near-end background noise via enhancement of residual echo

This paper examines the technique of using a noise suppressing nonlinearity in the adaptive filter error feedback loop of the acoustic echo canceler (AEC) based on the least mean square (LMS) algorithm when there are both double-talk and white background noise at the near-end. By combining the previously introduced noise suppressing technique with a compressive nonlinearity derived from the theory of robust statistics, consistently better results are obtained during double-talk as well as during single-talk when compared to the traditional approach of using only the compressive nonlinearity. It is shown that a compressive form of noise reducing nonlinearity can be derived also from the signal enhancement point of view when the noise probability density (pdf) is tailed more heavily and has a higher kurtosis than the Gaussian pdf. A combination of such a noise compressing nonlinearity and a noise suppressing nonlinearity is capable of producing results that are similar to that of the robust statistics approach during double-talk along with an added benefit of increased robustness during single-talk when there is only the background noise.