This paper introduces a new linear algebraic method for blind identification of a nonminimum phase FIR system. The proposed approach relies only on stationary fourth order statistics and is based on the 'joint diagonalization' of a set of fourth-order cumulant matrices. Its performance is illustrated via some numerical examples. Further this method turns out to overcome the problem of having some zero taps in the system impulse response.

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This paper presents a recursive time-varying adaptation step algorithm for updating the linear and quadratic coefficients vectors of a second-order Volterra filter. Simulations are carried in an equalization setup to compare the performance of this algorithm with other variable step least mean square (LMS) algorithms. The obtained results show that this algorithm brings substantial increase in the adaptation speed while keeping simplicity of the conventional LMS algorithm.

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Abstract:Using model reduction, an efficient low order (ARMA) modeling process for speech is presented. In this approach, the modeling process starts with a relatively high order (AR) model obtained by some classical methods. The AR model is then reduced using the SVD-based method. The model reduction yields a reduced order ARMA model which interestingly preserves the key properties of the original full order model such as stability. Line spectral frequencies LSF and signal-to-noise ratio (SNR) behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some simulations are conducted on some practical speech segments, such as phonemes and sentences.