A dynamic system approach to speech enhancement using the H∞ filtering algorithm

This paper presents a new approach to speech enhancement based on the H_∞ filtering. This approach differs from the traditional modified Wiener/Kalman filtering approach in the following two aspects: (1) no a priori knowledge of the noise source statistics is required, the only assumption made is that noise signals have a finite energy; (2) the estimation criterion for the filter design is to minimize the worst possible amplification of the estimation error signals in terms of the modeling errors and additive noise. Since most additive noise in speech are nonGaussian, this estimation approach is highly robust and more appropriate in practical speech enhancement. The proposed approach is straightforward to implement, as detailed in this paper. Experimental results show consistently superior enhancement performance of the H_∞ filtering algorithm over the Kalman filtering counterpart, measured by the global signal-to-noise ratio (SNR). Examination of the spectrogram displays for the enhanced speech shows that the H_∞ filtering approach tends to be more effective where the assumptions on the noise statistics are less valid