By Saeed V. Vaseghi
ISBN-10: 0470841621
ISBN-13: 9780470841624
ISBN-10: 0471626929
ISBN-13: 9780471626923
Content material:
Chapter 1 creation (pages 1–28):
Chapter 2 Noise and Distortion (pages 29–43):
Chapter three chance types (pages 44–88):
Chapter four Bayesian Estimation (pages 89–142):
Chapter five Hidden Markov types (pages 143–177):
Chapter 6 Wiener Filters (pages 178–204):
Chapter 7 Adaptive Filters (pages 205–226):
Chapter eight Linear Prediction versions (pages 227–262):
Chapter nine energy Spectrum and Correlation (pages 263–296):
Chapter 10 Interpolation (pages 297–332):
Chapter eleven Spectral Subtraction (pages 333–354):
Chapter 12 Impulsive Noise (pages 355–377):
Chapter thirteen brief Noise Pulses (pages 378–395):
Chapter 14 Echo Cancellation (pages 396–415):
Chapter 15 Channel Equalization and Blind Deconvolution (pages 416–466):
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Extra resources for Advanced Digital Signal Processing and Noise Reduction, Second Edition
Sample text
The distortion, due to an insufficiently high sampling rate, is irrevocable and is known as aliasing. This observation is the basis of the Nyquist sampling theorem which states: a band-limited continuous-time signal, with a highest 24 Introduction Time domain Frequency domain x(t) X(f) 0 B –B t f 2B × * Impulse-train-sampling function ... Ts = xp(t) P( f ) = ∞ ∑ δ ( f − kF ) s k = −∞ ... t 0 –Fs = Fs=1/Ts Xp ( f ) Impulse-train-sampled signal ... * ... –Fs/2 t f 0 × f Fs/2 SH( f ) sh(t) Sample-and-hold function ...
1975) Adaptive Noise Cancelling: Principles and Applications. Proc. IEEE, 63, pp. 1692-1716. WIENER N. (1948) Extrapolation, Interpolation and Smoothing of Stationary Time Series. MIT Press, Cambridge, MA. WIENER N. (1949) Cybernetics. MIT Press, Cambridge, MA. A. A. (1963) Linear System Theory: The StateSpace Approach. McGraw-Hill, NewYork. Advanced Digital Signal Processing and Noise Reduction, Second Edition. Saeed V. 10 Thermal Noise Shot Noise Electromagnetic Noise Channel Distortions Modelling Noise oise can be defined as an unwanted signal that interferes with the communication or measurement of another signal.
Y(N-1) WN -1 ^ X(0) ^ X(1) ^ X(2) ^ X(N-1) Inverse Discrete Fourier Transform Restored signal ^ x(0) ^ x(1) ^ x(2) . . 4 A frequency−domain Wiener filter for reducing additive noise. 8 Introduction noisy signal is available. The filter bank coefficients attenuate each noisy signal frequency in inverse proportion to the signal–to–noise ratio at that frequency. The Wiener filter bank coefficients, derived in Chapter 6, are calculated from estimates of the power spectra of the signal and the noise processes.
Advanced Digital Signal Processing and Noise Reduction, Second Edition by Saeed V. Vaseghi
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