Toeplitz covariance

A variety of within-subject covariance matrices have been proposed (Table 6.2). The most common are the simple, unstructured, compound symmetry, first-order autoregressive [referred to as AR(1)], and Toeplitz covariance. The simple covariance assumes that observations within a subject are uncorrelated and have constant variance, like in Eq. (6.28). Unstructured assumes... 

On-line estimation of the second-order statistics of a speech signal from a sample function of the noisy signal has proven to be a better choice. Since the analysis frame length is usually relatively small, the covariance matrices of the speech signal maybe assumed Toeplitz. Thus, the autocorrelation function of the clean signal in each analysis frame must be estimated. The Fourier transform of a windowed autocorrelation function estimate provides estimates of the variances of the clean signal spectral components. 

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