We present a new method for estimation of spectrum transition of nonstationary signals in cases of low signal-to-noise ratio (SNR). Instead of the basic functions employed in the previously proposed time-varying autoregressive (AR) modeling, we introduce a spectrum transition constraint into the cost function described by the partial correlation (PARCORR) coefficients so that the method is applicable to noisy nonstationary signals of which spectrum transition patterns are complex. By applying this method to the analysis of vibration signals on the interventricular septum (IVS) of the heart, noninvasively measured by the novel method developed in our laboratory using ultrasonics, the spectrum transition pattern is clearly obtained during one cardiac cycle for normal subjects and a patient with cardiomyopathy.

We present an extended quarter-plane lattice model for generating two-dimensional (2-D) autoregressive fields. This work is a generalization of the extended lattice filter of diagonal form (ELDF) developed by Ertuzun et al. The proposed model represents a wider class of 2-D AR fields than conventional lattice models. Several examples are presented to demonstrate the applicability of the proposed model. Furthermore, the proposed structure is compared with other conventional lattice filters based on the computation of their entropy values.

The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.

In this paper, a new M-estimation technique for the linear prediction analysis of speech is proposed. Since in the conventional linear prediction (CLP) method the obtained estimates are very much affected by the large amplitude residual parts, in the proposed method we use a loss function which assigns large weighting factor for small amplitude residuals and small weighting factor for large amplitude residuals which is for instance caused by the pitch excitations. The loss function is based on the assumption that the residual signal has an independent and identical t-distribution t(α) with α degrees of freedom. The efficiency of this new estimator depends on α. When α=, we get the CLP method. When the proposed method with small α is applied to the problems of estimating the formant frequencies and bandwidths of the synthetic speech by finding the roots of the prediction polynomial, we can achieve a more accurate and a smaller standard deviation (SD) estimate than that with large α. When the signal is very spiky, the proposed method can ahieve more efficient and accurate estimates than that with robust linear prediction (RBLP) method. The loss function is modified in the similar manner as the autocorrelation method. The solution is calculated by the Newton-Raphson iteration technique. The simulation results show that only few iterations are needed to reach a stationary point, the stationary point is always a local minimum and the obtained prediction filter is always minimum phase. Preliminary experiments on the human speech data indicate that the obtained results are insensitive to the placement of the analysis window and a higher spectral resolution than the CLP and RBLP method can be achieved.