A new identification algorithm for allpass systems by higher-order statistics

Chi, Chong-Yung; Kung, Jung-Yuan

doi:10.1016/0165-1684(93)e0019-h

Cited by 31 publications

(20 citation statements)

References 14 publications

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“…Noninvertible ARMA models have appeared, for example, in vocal tract filters [8,9] and in the analysis of unemployment rates [16]. We use them in this paper in the deconvolution of a simulated water gun seismogram.…”

Section: Introductionmentioning

confidence: 99%

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Maximum likelihood estimation for all-pass time series models

Andrews

Davis

Breidt

2006

Journal of Multivariate Analysis

125

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An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximate likelihood for a causal all-pass model is given and used to establish asymptotic normality for maximum likelihood estimators under general conditions. Behavior of the estimators for finite samples is studied via simulation. A two-step procedure using all-pass models to identify and estimate noninvertible autoregressive-moving average models is developed and used in the deconvolution of a simulated water gun seismogram.

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Section: Introductionmentioning

confidence: 99%

“…Hence, cumulant-based estimators, using cumulants of order greater than two, are often used to estimate such models [8,9,12]. Breidt et al [6] consider a least absolute deviations (LAD) approach which is motivated by the approximate likelihood of an all-pass model with Laplace (two-sided exponential) noise.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation for all-pass time series models

Andrews

Davis

Breidt

2006

Journal of Multivariate Analysis

125

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“…In such scenario, some prior knowledge or assumption on either or is required to approach the problem. Chi et al assumed to be a non-Gaussian i.i.d sequence with a dominant th order cumulant and were estimated by maximizing this cumulant [49], [50]. This method requires prior knowledge about the order of cumulant to be maximized.…”

Section: Allpass Modeling Of Phase Spectrum Of Speechmentioning

confidence: 99%

Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

Vijayan¹,

Murty²

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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The phase spectrum of Fourier transform has received lesser prominence than its magnitude counterpart in speech processing. In this paper, we propose a method for parametric modeling of the phase spectrum, and discuss its applications in speech signal processing. The phase spectrum is modeled as the response of an allpass (AP) filter, whose coefficients are estimated from the knowledge of speech production process, especially the impulse-like nature of excitation source. A signal retaining only the phase spectral component of speech signal is derived by suppressing the magnitude spectral component, and is modeled as the output of an AP filter excited with a sequence of impulses. Entropy of energy of the input signal is minimized to estimate the coefficients of the AP filter. The resulting objective function, being nonconvex in nature, is minimized using particle swarm optimization. The group delay response of estimated AP filters can be used for accurate analysis of resonances of the vocal-tract system (VTS). The error signal associated with AP modeling provides unambiguous evidence about the instants of significant excitation of the VTS. The applications of the proposed AP modeling include, but not limited to, formant tracking, extraction of glottal closure instants, speaker verification and speech synthesis.

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“…Many algorithms exist, in the literature for the identification of the non-minimum phase FIR system using higher order cumulants [2], [3], [5], [6], [11], [14−17], [19]. However, identification of linear time-invariant (LTI) systems with only output measurements is very important in many signal processing areas such as seismic deconvolution, channel equalization (in communications), radar, sonar, oceanography, speech signal processing, and image processing [6], [7]. In this work, we have principally focussed in channel impulse response estimation such as: magnitude and phase.…”

Section: Introductionmentioning

confidence: 99%

Comparative study between blind identification algorithms and least mean square algorithm for non minimum phase channel

Zidane

Safi

Sabri

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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In this paper we present a comparative study between four algorithms for identification of linear non minimum phase systems in noise environment case. The input sequence is assumed to be independent and identically distributed (i.i.d), zero mean and must be non-gaussian. In order to test the efficiency of these algorithms we have selected the Macchi channel. The simulation results in noisy environment and for different data input channel, demonstrates that the presented methods could estimate the phase and magnitude with different precision.

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A new identification algorithm for allpass systems by higher-order statistics

Cited by 31 publications

References 14 publications

Maximum likelihood estimation for all-pass time series models

Maximum likelihood estimation for all-pass time series models

Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

Comparative study between blind identification algorithms and least mean square algorithm for non minimum phase channel

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