By Saeed V. Vaseghi
Chapter 1 creation (pages 1–28):
Chapter 2 Noise and Distortion (pages 29–43):
Chapter three chance 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):
Read or Download Advanced Digital Signal Processing and Noise Reduction, Second Edition PDF
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Additional info for Advanced Digital Signal Processing and Noise Reduction, Second Edition
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.
1960) A New Approach to Linear Filtering and Prediction Problems. Trans. of the ASME, Series D, Journal of Basic Engineering, 82, pp. 35–45. M. (1993) Fundamentals of Statistical Signal Processing, Estimation Theory. Prentice-Hall, Englewood Cliffs, NJ. S. (1983) Speech Enhancement. Prentice Hall, Englewood Cliffs, NJ. , SALZ J. J. (1968) Principles of Data Communications. McGraw-Hill, New York. Y. (1993) Digital Neural Networks. Prentice-Hall, Englewood Cliffs, NJ. L. (1987) Digital Spectral Analysis with Applications.
Other instances of shot noise are the flow of photons in a laser beam, the flow and recombination of electrons and holes in semiconductors, and the flow of photoelectrons emitted in photodiodes. The concept of randomness of the rate of emission or arrival of particles implies that shot noise can be modelled by a Poisson distribution. When the average number of arrivals during the observing time is large, the fluctuations will approach a Gaussian distribution. Note that whereas thermal noise is due to “unforced” random movement of particles, shot noise happens in a forced directional flow of particles.