Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

Mahmmod, BM, Ramli, AR, Baker, T, Al-Obeidat, F, Abdulhussain, SH and Jassim, WA (2019) Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials. IEEE Access. ISSN 2169-3536

[img]
Preview
Text
Final Article.pdf - Accepted Version

Download (8MB) | Preview

Abstract

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures.

Item Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 18 Jul 2019 08:28
Last Modified: 01 Aug 2019 10:00
DOI or Identification number: 10.1109/ACCESS.2019.2929864
URI: http://researchonline.ljmu.ac.uk/id/eprint/11065

Actions (login required)

View Item View Item