Advanced Digital Signal Processing and Noise Reduction
Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distortion is an important part of a signal processing system.
Advanced Digital Signal Processing and Noise Reduction, Third Edition, provides a fully updated and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Noise is the eternal bane of communications engineers, who are always striving to find new ways to improve the signal-to-noise ratio in communications systems and this resource will help them with this task.
* Features two new chapters on Noise, Distortion and Diversity in Mobile Environments and Noise Reduction Methods for Speech Enhancement over Noisy Mobile Devices.
* Topics discussed include: probability theory, Bayesian estimation and classification, hidden Markov models, adaptive filters, multi-band linear prediction, spectral estimation, and impulsive and transient noise removal.
* Explores practical solutions to interpolation of missing signals, echo cancellation, impulsive and transient noise removal, channel equalisation, HMM-based signal and noise decomposition.
This is an invaluable text for senior undergraduates, postgraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also appeal to engineers in telecommunications and audio and signal processing industries.
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2 Noise and Distortion
3 Probability and Information Models
4 Bayesian Inference
5 Hidden Markov Models
6 Least Square Error Filters
7 Adaptive Filters
8 Linear Prediction Models
11 Spectral Amplitude Estimation
12 Impulsive Noise
13 Transient Noise Pulses
14 Echo Cancellation
15 Channel Equalisation and Blind Deconvolution
16 Speech Enhancement in Noise
17 Noise in Wireless Communications
9 Power Spectrum and Correlation
Other editions - View all
acoustic feedback algorithm amplitude antennas applications Assuming bandwidth Bayesian binary channel equalisation channel input Chapter coefficient vector communications systems considered correlation covariance matrix defined Digital Signal Processing echo cancellation Equation excitation filter coefficients Fourier transform frequency response Gaussian process given hidden Markov model IEEE impulse response impulsive noise input signal interpolation inverse Kalman filter least square error likelihood linear prediction linear prediction model MAP estimate mean square error method microphone missing samples mobile noise process noise reduction noisy signal noisy speech nonlinear nonstationary observation obtained parameter vector polynomial posterior power spectrum prediction error predictor coefficients probability density function quantisation random process random signal random variable sequence signal and noise signal vector signal xm sinusoidal spectral subtraction speech and noise speech recognition speech signal stationary stationary process statistical sub-band time-varying transient noise pulses uncorrelated variance white noise Wiener filter zero