Nonminimum phase non-Gaussian autoregressive processes.

Deconvolution and Vocal-tract Parameter Estimation of Speech Signals by Higher-order Statistics Based Inverse Filters

In this paper, we propose a two-step method for deconvolution and vocal-tract parameter estimation of (nonGaussian) voiced speech signals. In the first step, the driving input (a non-Gaussian pseudo-periodic positive pulse train) to the vocal-tract filter which can be nonminimum-phase is estimated from speech data by a hagher-order statistics (HOS) based inverse fi lter. I n the second step, au...

Autoregressive Gaussian Random Vectors of First Order

Identification of noncausal nonminimum phase AR models using higher-order statistics

In this paper, we address the problem of estimating the parameters of a noncausal autoregressive (AR) signal from estimates of the higher-order cumulants of noisy observations. The proposed family of techniques uses both 3rd-order and 4thorder cumulants of the observed output data. Consequently, at low SNR, they provide superior performance to methods based on autocorrelations. The measurement ...

Parametric Cumulant Based Phase Estimation Of 1-D And 2-D Nonminimum Phase Systems By Allpass Filter - Signal Processing, IEEE Transactions on

This paper proposes a parametric cumulant-based phase-estimation method for one-dimensional (1-D) and twodimensional (2-D) linear time-invariant (LTI) systems with only non-Gaussian measurements corrupted by additive Gaussian noise. The given measurements are processed by an optimum allpass filter such that a single M th-order (M 3) cumulant of the allpass filter output is maximum in absolute v...

Cumulant based identification approaches for nonminimum phase FIR systems

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the secondand third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identificatio...