Advanced Digital Signal Processing and Noise Reduction, by Saeed V. Vaseghi

By Saeed V. Vaseghi

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1986) Adaptive Signal Processing Theory and Applications. Springer-Verlag, New York. B. L. (1958) An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, New York. 22 INTRODUCTION Ephraim Y. (1992) Statistical model based speech enhancement systems. Proc. IEEE, 80(10): 1526–1555. G. (1968) Information Theory and Reliable Communication. Wiley, New York. G. (1963) Theory of Motion of Heavenly Bodies. Dover, New York. Haykin S. (1985) Array Signal Processing. Prentice-Hall, Englewood Cliffs, NJ.

10) where bˆ m is an estimate of the binary state indicator sequence b m , and may be erroneous, particularly if the signal-to-noise ratio is low. 1 lists four possible outcomes that, together, b m and its estimate, bˆ m , can assume. The choice of the threshold level affects the sensitivity of the detector. The higher the threshold, the lower the likelihood that noise would be classified as signal is, so the false alarm rate falls, but the probability of misclassification of signal as noise increases.

2 SIGNAL PROCESSING METHODS Signal processing methods have evolved in algorithmic complexity, aiming for optimal utilisation of the information in order to achieve the best performance. In general the computational requirement of signal processing methods increases, often exponentially, with the algorithmic complexity. However, the implementation cost of advanced signal processing methods has been offset and made affordable by the consistent trend in recent years of a continuing increase in the performance, coupled with a simultaneous decrease in the cost, of signal processing hardware.

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