By Saeed V. Vaseghi
Chapter 1 advent (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 resources for Advanced Digital Signal Processing and Noise Reduction, Second Edition
3 Bayesian Statistical Signal Processing The fluctuations of a purely random signal, or the distribution of a class of random signals in the signal space, cannot be modelled by a predictive equation, but can be described in terms of the statistical average values, and modelled by a probability distribution function in a multidimensional signal space. For example, as described in Chapter 8, a linear prediction model driven by a random signal can model the acoustic realisation of a spoken word. However, the random input signal of the linear prediction model, or the variations in the characteristics of different acoustic realisations of the same word across the speaking population, can only be described in statistical terms and in terms of probability functions.
The adaptive noise canceller is more effective in cancelling out the low-frequency part of the noise, but generally suffers from the non-stationary character of the signals, and from the oversimplified assumption that a linear filter can model the diffusion and propagation of the noise sound in the space. In many applications, for example at the receiver of a telecommunication system, there is no access to the instantaneous value of the contaminating noise, and only the noisy signal is available.
3 Configuration of a two-microphone adaptive noise canceller. 7 Applications of Digital Signal Processing as input the noisy signal x(m) + n(m) , and a second directional microphone, positioned some distance away, measures the noise α n(m + τ ) . The attenuation factor α and the time delay τ provide a rather over-simplified model of the effects of propagation of the noise to different positions in the space where the microphones are placed. The noise from the second microphone is processed by an adaptive digital filter to make it equal to the noise contaminating the speech signal, and then subtracted from the noisy signal to cancel out the noise.