Advanced Signal Processing and Digital Noise Reduction
Noise cancellation is particularly important in the new mobile communications field, with respect to background noise and acoustic interference in moving vehicles. This comprehensive text develops a coherent and structured presentation of a broad range of the theory and application of statistical signal processing, with emphasis on digital noise reduction algorithms. Other applications covered are spectral estimation, channel equalisation, speech coding over noisy channels, speech recognition in adverse environments, active noise control, echo cancellation, restoration of lost filters, and adaptive notch filters.
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Bayesian Estimation and Classification
Hidden Markov Models
11 other sections not shown
acoustic feedback adaptive equaliser additive noise algorithm amplitude applications Assuming average Bayesian binary binary-state blind equalisation channel distortion channel equalisation channel output chapter coefficient vector convergence covariance matrix defined denotes desired signal detection echo cancellation eigenvalues equation error signal excitation signal filter coefficients Fourier transform frequency domain frequency response Gaussian pdf Gaussian process given hidden Markov model illustrated in Figure impulse response impulsive noise interpolation inverse filter Kalman Kalman filter least squared error likelihood linear prediction model MAP estimate maximum mean squared error method minimisation missing samples MMSE noise process noisy signal nonlinear nonstationary obtained Pages parameter vector phase polynomial power spectrum prediction error predictor coefficients probability density function quantisation random process random signal recursive signal processing signal restoration signal space signal x(m sinusoids spectral estimation spectral subtraction speech recognition stationary stationary process statistical stochastic process time-varying transient noise pulses uncorrelated variance Wiener filter