## Optimum signal processing: an introductionGood,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine. |

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### Contents

SOME SIGNAL PROCESSING APPLICATIONS | 55 |

SPECTRAL FACTORIZATION | 97 |

LINEAR ESTIMATION OF SIGNALS | 110 |

Copyright | |

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### Common terms and phrases

Acoust adaptive filter AM(z Analysis Ap(z array autocorrelation lags autocorrelation matrix autoregressive Burg's method causal component convergence correlation canceler corresponding covariance matrix criterion data compression deconvolution defined density desired signal diagonal difference equation Digital discussed eigenvalue eigenvectors ep(n example extract Figure filter B(z filter weights follows frequency gapped function gaussian gradient IEEE Trans impulse response input inside the unit iteration Kalman filter lattice filter least-squares Levinson recursion linear prediction matrix form minimal-phase minimization model parameters noise canceling normal equations obtained optimal filter order predictor orthogonality equations output periodogram polynomial prediction problem prediction-error filter Proc quantity quantization random signal random variables reflection coefficients reflection response sample autocorrelation Section sequence Show shown in Fig signal model Signal Process sinusoid solution spectrum estimate Speech spiking filter subroutine subspace synthesis filter transfer function uncorrelated unit circle variance vector white noise Wiener filter Yule-Walker z-transform zero-mean zeros