Speech enhancement using Rao–Blackwellized particle filtering of complex DFT coefficients. (October 2018)

• Record Type: Journal Article
• Authors: Meddah, Mounir; Amrouche, Abderrahmane; Taleb-Ahmed, Abdelmalik
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 847
• Page End: 861
• Publication Date: 2018-10
• Subjects: Speech enhancement -- Sequential Monte Carlo -- Rao-Blackwellized particle filter -- Complex DFT coefficients -- DFT-RBPF; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.024
• Languages: English
• ISSNs: 0045-7906

Abstract

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.