An efficient frequency-domain adaptive forward BSS algorithm for acoustic noise reduction and speech quality enhancement. (May 2016)

• Record Type: Journal Article
• Authors: Djendi, Mohamed
• Is Part Of: Computers & electrical engineering. Volume 52(2016)
• Page Start: 12
• Page End: 27
• Publication Date: 2016-05
• Subjects: Adaptive filtering -- Speech enhancement -- Frequency-domain -- Time-domain -- Decorrelation; 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.2016.04.006
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We propose a new Frequency-domain adaptive decorrelating algorithm. The proposed algorithm improves convergence speed even with long adaptive filters. The new algorithm is efficient in Speech quality Enhancement and Acoustic Noise Reduction applications. The proposed algorithm distorts less the speech signal at the output. Abstract: In this paper, we consider the speech enhancement and acoustic noise reduction problem in a moving car through a blind source separation scheme employing two loosely spaced microphones. We propose a new efficient frequency domain-symmetric adaptive decorrelation (FD-SAD) algorithm that removes punctual noise components from noisy speech signals. The FD-SAD algorithm is combined with the forward blind source separation FBSS structure to enhance the performances of its time-domain symmetric adaptive decorelating (TD-SAD) version. The proposed algorithm has a good tracking behaviour and fast convergence speed even in very noisy conditions with loosely spaced microphones. Intensive experiments have been done on the newly proposed algorithm in terms of the Segmental Signal-to-Noise-Ratio (SegSNR), the System Mismatch (SM), the Segmental Mean Square Error (SegMSE), and the Cepstral Distance (CD) criteria. The comparison results with the state-of-the-art algorithms have highlighted the excellent performance of the proposed algorithm, and have shown its ability to completely remove the correlated noise components from speech signal even in very noisy conditions when controlled by a voice activity detector. Graphical abstract: