By Saeed V. Vaseghi
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 versions (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 strength 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 temporary 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
Radio channels, such as those at microwave frequencies used by cellular mobile phone operators, are particularly sensitive to the propagation characteristics of the channel environment. g. quantisation noise in digital coding of speech or image signals, or lost data packets in digital data communication systems. 31 White Noise Depending on its frequency or time characteristics, a noise process can be classified into one of several categories as follows: (a) Narrowband noise: a noise process with a narrow bandwidth such as a 50/60 Hz ‘hum’ from the electricity supply.
This behaviour may be attributed to the non-linear modes of response of the electro-mechanical playback system excited by the physical scratch discontinuity. Observations of many scratch waveforms from damaged gramophone records reveals that they have a well-defined profile, and can be characterised by a relatively small number of typical templates. Scratch pulse modelling and removal is considered in detain in Chapter 13. 6 Thermal Noise Thermal noise, also referred to as Johnson noise (after its discoverer J.
6 A block diagram illustration of the classifier in a binary phase-shift keying demodulation. 10 Introduction M1 likelihood of M 1 fY|M (Y| M 1 ) Word model Speech signal Feature extractor Feature sequence Y M2 likelihood of M 2 fY|M (Y| M 2 ) . . 7 Configuration of speech recognition system, f(Y|Mi) is the likelihood of the model Mi given an observation sequence Y. states for the bit “1” and the bit “0”. The decoder correlates the unlabelled input signal with each of the two candidate symbols and selects the candidate that has a higher correlation with the input.
Advanced Digital Signal Processing and Noise Reduction, Second Edition by Saeed V. Vaseghi