Speech enhancement using Rao–Blackwellized particle filtering of complex DFT coefficients
Résumé
An improved method for speech enhancement, which is based on particle filtering, is presented in this paper. The Rao–Blackwellized particle filter (RBPF) is used to estimate the model parameters and recover a clean speech signal. The proposed method (named DFT-RBPF) enhances the complex discrete Fourier transform (DFT) coefficients of a noisy speech signal. The real and imaginary parts are filtered separately, under the assumption of mutual independence, using a low-order time-varying auto-regressive (TVAR) process with a linear Gaussian model. The obtained results, in terms of the coherence speech intelligibility index (CSII), perceptual evaluation of speech quality (PESQ), segmental and overall signal-to-noise ratios (SNRseg, SNR), demonstrate the improved performance of the proposed method, when compared with the recent methods based on particle filters and the existing algorithms for speech enhancement.