Speech enhancement using voiced speech probability based wavelet decomposition. (August 2017)

• Record Type: Journal Article
• Authors: Bhowmick, Anirban; Chandra, Mahesh
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 706
• Page End: 718
• Publication Date: 2017-08
• Subjects: VSP -- Wavelet decomposition -- Reconstruction -- SNR improvement -- Thresholding; 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.2017.01.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A Wavelet based speech enhancement system is proposed using voiced speech probability (VSP) based frame separation. A spectral gain is estimated for noise minimization. 4 level of wavelet decomposition and SURE thresholding is used for denoising. A subset of TIMIT sentences database is used, corrupted with five noise conditions. Proposed method outperforms other compared techniques in terms of SNR improvement. Graphical abstract: Graphical description of the proposed technique. Abstract: Wavelet-based speech enhancement technique has gained popularity for its inherent nature of noise minimization. Utilizing this advantage, in this work, a new approach is taken to design a speech enhancement system by using Voiced Speech Probability (VSP) based Wavelet Decomposition (WD). The VSP-based improved Voice Activity Detector (VAD) is designed to separate voiced/unvoiced frames on the basis of the probability calculated by the likelihood ratio of two Gaussian Mixture Models (GMMs). A gain estimator is integrated with the WD stage to minimize the Mean Square Error (MSE). This proposed method has been evaluated on a subset of TIMIT database corrupted with five different noise conditions such as machine gun noise, white noise, pink noise, f16 noise, and operations room noise at four Signal to Noise Ratio (SNR) levels. This method is also compared with traditional algorithms, and significant performance improvement is observed.