Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03428142
Contributor : Kathleen Torck Connect in order to contact the contributor
Submitted on : Monday, November 15, 2021 - 8:45:43 AM
Last modification on : Tuesday, November 16, 2021 - 3:56:50 AM

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

12