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2018•International Journal of Information and Communication TechnologyRequires access

De-noising by Gammachirp and Wiener filter-based methods for speech enhancement

H. Rahali, Zied Hajaiej, Noureddine Ellouze

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Abstract

In this paper, we propose a method for enhancing of speech corrupted by noise. The new speech enhancement approach combines RASTA, Wiener filter (WF) and the Gammachirp filter (GF) in series connection to construct a two-stage hybrid system (named RASTA-WF-GF) in frequency domain to enhance the speech with additive noise. It is shown that the proposed method significantly outperforms, spectral subtraction (SS), Wiener filter (WF), Kalman filter (KF) and RASTA speech enhancement methods, in the presence of noise.

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What this paper is about

In this paper, we propose a method for enhancing of speech corrupted by noise. The new speech enhancement approach combines RASTA, Wiener filter (WF) and the Gammachirp filter (GF) in series connection to construct a two-stage hybrid system (named RASTA-WF-GF) in frequency domain to enhance the speech with additive noise. It is shown that the proposed method significantly outperforms, spectral subtraction (SS), Wiener filter (WF), Kalman filter (KF) and RASTA speech enhancement methods, in the presence of noise.

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Available abstract

In this paper, we propose a method for enhancing of speech corrupted by noise. The new speech enhancement approach combines RASTA, Wiener filter (WF) and the Gammachirp filter (GF) in series connection to construct a two-stage hybrid system (named RASTA-WF-GF) in frequency domain to enhance the speech with additive noise. It is shown that the proposed method significantly outperforms, spectral subtraction (SS), Wiener filter (WF), Kalman filter (KF) and RASTA speech enhancement methods, in the presence of noise.

Key concepts: Wiener filter, Speech enhancement, Computer science, Speech recognition, Wiener deconvolution, Noise (video), Filter (signal processing), Kalman filter

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