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Bayesian Estimation and Classification
Hidden Markov Models
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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 filter least squared error likelihood linear prediction model MAP estimate maximum mean squared error method minimisation missing samples mixture Gaussian 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 variance Wiener filter