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Speech enhancement using Rao–Blackwellized particle filtering of complex DFT coefficients

Abstract : 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.
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Mounir Meddah, Abderrahmane Amrouche, Abdelmalik Taleb-Ahmed. Speech enhancement using Rao–Blackwellized particle filtering of complex DFT coefficients. Computers and Electrical Engineering, Elsevier, 2018, 71, pp.847-861. ⟨10.1016/j.compeleceng.2017.07.024⟩. ⟨hal-03428142⟩



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