Robust Parametric Modeling of Speech in Additive White Gaussian Noise

• 作者: Abdelaziz Trabelsi Otmane Ait Mohamed Yves Audet
• 来源期刊: 信号与信息处理(英文)
• 学科: 数学
• 关键词: ARMA Model Noise Variance Overdetermined PARAMETRIC Evaluation SINGULAR Value Representation LMS Technique Yule-Walker EQUATIONS
• 年，卷（期）: 2015,（2）
• 页码范围: 99-108
• 页数: 10页
• 分类号: O1

摘要

In estimating the linear prediction coefficients for an autoregressive spectral model, the concept of using the Yule-Walker equations is often invoked. In case of additive white Gaussian noise (AWGN), a typical parameter compensation method involves using a minimal set of Yule-Walker equation evaluations and removing a noise variance estimate from the principal diagonal of the autocorrelation matrix. Due to a potential over-subtraction of the noise variance, however, this method may not retain the symmetric Toeplitz structure of the autocorrelation matrix and thereby may not guarantee a positive-definite matrix estimate. As a result, a significant decrease in estimation performance may occur. To counteract this problem, a parametric modelling of speech contaminated by AWGN, assuming that the noise variance can be estimated, is herein presented. It is shown that by combining a suitable noise variance estimator with an efficient iterative scheme, a significant improvement in modelling performance can be achieved. The noise variance is estimated from the least squares analysis of an overdetermined set of p lower-order Yule-Walker equations. Simulation results indicate that the proposed method provides better parameter estimates in comparison to the standard Least Mean Squares (LMS) technique which uses a minimal set of evaluations for determining the spectral parameters.