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

By Saeed V. Vaseghi

Electronic sign processing performs a significant position within the improvement of contemporary communique and data processing structures. the idea and alertness of sign processing is anxious with the identity, modelling and utilisation of styles and buildings in a sign method. The remark signs are usually distorted, incomplete and noisy and consequently noise relief, the elimination of channel distortion, and substitute of misplaced samples are very important elements of a sign processing method.

The fourth version of Advanced electronic sign Processing and Noise Reduction updates and extends the chapters within the earlier variation and comprises new chapters on MIMO platforms, Correlation and Eigen research and self sufficient part research. the big variety of themes coated during this booklet contain Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and elimination of impulsive and brief noise, interpolation of lacking facts segments, speech enhancement and noise/interference in cellular conversation environments. This publication offers a coherent and established presentation of the speculation and functions of statistical sign processing and noise aid methods.

  • Two new chapters on MIMO structures, correlation and Eigen research and self sufficient part analysis

  • Comprehensive insurance of complicated electronic sign processing and noise relief tools for verbal exchange and knowledge processing systems

  • Examples and purposes in sign and knowledge extraction from noisy data

  • Comprehensive yet available assurance of sign processing concept together with chance types, Bayesian inference, hidden Markov types, adaptive filters and Linear prediction models

Advanced electronic sign Processing and Noise Reduction is a useful textual content for postgraduates, senior undergraduates and researchers within the fields of electronic sign processing, telecommunications and statistical information research. it is going to even be of curiosity to expert engineers in telecommunications and audio and sign processing industries and community planners and implementers in cellular and instant conversation communities.

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Advanced Signal Processing and Digital Noise Reduction

Electronic sign processing performs a relevant function within the improvement of recent verbal exchange and data processing structures. the idea and alertness of sign processing is worried with the id, modelling and utilisation of styles and buildings in a sign procedure. The statement indications are frequently distorted, incomplete and noisy and hence noise aid, the removing of channel distortion, and substitute of misplaced samples are very important elements of a sign processing process.

Additional resources for Advanced Signal Processing and Digital Noise Reduction

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16. 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 lossless operation, in that the analog signal can be recovered from 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.

Springer-Verlag, New York. DAVENPORT W. , ROOT W. L. (1958), An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, New York. , (1992),Statistical Model Based Speech Enhancement Systems, Proc. IEEE, Vol. 80, No. 10, Pages 1526-1555. GAUSS K. G. (1963), Theory of Motion of Heavenly Bodies, Dover, New York. GALLAGER R. G. (1968), Information Theory and Reliable Communication, Wiley, New York. HAYKIN S. (1991), Adaptive Filter Theory, Prentice-Hall, Englewood Cliffs, N. J. HAYKIN S.

31) 34 Stochastic Processes where Ilx, rxx(m), and Pxxif) are the mean value, the autocorrelation and the power spectrum of the signal x(m) respectively, and Xif,m) denotes the frequency-time spectrum of x(m). 2 Wide Sense Stationary Processes The strict sense stationarity condition requires that all of the statistics of the process should be time-invariant. A less restrictive form of a stationary process is the so called wide sense stationarity. 33) From the definitions of strict and wide sense stationary processes, it is clear that a strict sense stationary process is also wide sense stationary, whereas the reverse is not necessarily true.

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