Real-world particle filtering-based speech enhancement

This paper presents a viable particle filtering (PF) solution for single microphone speech enhancement in real-world conditions, i.e., operating at low SNR in nonstationary noise environments, while remaining computationally tractable. The enhancement takes place in the subband domain with elementary PFs in each band. To efficiently handle complex noise situations, the noise spectrum is modelled in each band as a white Gaussian noise sequence with a time-varying gain. Two solutions are proposed to estimate these time-varying average subband noise levels: they are either drawn internally by the PFs, or they are obtained by external dedicated noise power spectral density estimation - both methods are found to yield very close results. Several subband decompositions are tested, and a robust way of incorporating perceptual constraining is introduced. The assembled PF-based architecture is then compared with state-of-the-art enhancement algorithms in various conditions, and is found to outperform them according to seven objective speech quality measures.