Residual noise control using a parametric multichannel Wiener filter

Multichannel noise reduction techniques are commonly used in speech communication applications. In these applications, it is often desired to maintain a residual amount of background noise to avoid perceptually unpleasant artifacts, such as musical tones or time periods of complete silence. Noise reduction can be achieved by the parametric multichannel Wiener filter (PMWF), which provides a trade-off between speech distortion and noise reduction. To additionally control the maximum noise reduction, the PMWF can be decomposed into a spatial filter and a spectral gain, which is limited to a desired minimum value. Such decomposition is however only possible if the desired source power spectral density matrix is rank-one, which in general does not even hold for a single source in reverberant environments. In the proposed approach, we define the desired signal as a sum of the speech signal plus the desired residual noise, and derive an optimum filter in the minimum mean-square error sense. The resulting filter has the advantage that it enables direct control of the maximum noise reduction without the need for a gain limiting step and is furthermore applicable to desired signals of higher rank. We analyze the derived filter thoroughly and show its relation to the standard PMWF that results as a special case. Furthermore, we propose a solution for keeping the residual noise level constant in slowly time-varying noise fields.