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

By Saeed V. Vaseghi

Content material:
Chapter 1 advent (pages 1–28):
Chapter 2 Noise and Distortion (pages 29–43):
Chapter three likelihood versions (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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Additional info for Advanced Digital Signal Processing and Noise Reduction, Second Edition

Example text

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.

1 Adaptive Noise Cancellation and Noise Reduction In speech communication from a noisy acoustic environment such as a moving car or train, or over a noisy telephone channel, the speech signal is observed in an additive random noise. In signal measurement systems the information-bearing signal is often contaminated by noise from its surrounding environment. 2 A classification of the applications of digital signal processing. where x (m) and n( m) are the signal and the noise, and m is the discretetime index.

1 Time-Domain Sampling and Reconstruction of Analog Signals The conversion of an analog signal to a sequence of n-bit digits consists of two basic steps of sampling and quantisation. The sampling process, when performed with sufficiently high speed, can capture the fastest fluctuations of the signal, and can be a loss-less operation in that the analog signal can be recovered through interpolation of the sampled sequence as described in Chapter 10. The quantisation of each sample into an n-bit digit, involves some irrevocable error and possible loss of information.

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