advanced digital signal processing and noise reduction(一本好书）

leoshan

下面时书的介绍，喜欢的下吧！
This book is organised in 15 chapters.
Chapter 1 begins with an introduction to signal processing, and
provides a brief review of signal processing methodologies and
applications. The basic operations of sampling and quantisation are
reviewed in this chapter.
Chapter 2 provides an introduction to noise and distortion. Several
different types of noise, including thermal noise, shot noise, acoustic noise,
electromagnetic noise and channel distortions, are considered. The chapter
concludes with an introduction to the modelling of noise processes.
Chapter 3 provides an introduction to the theory and applications of
probability models and stochastic signal processing. The chapter begins
with an introduction to random signals, stochastic processes, probabilistic
models and statistical measures. The concepts of stationary, non-stationary
and ergodic processes are introduced in this chapter, and some important
classes of random processes, such as Gaussian, mixture Gaussian, Markov
chains and Poisson processes, are considered. The effects of transformation
of a signal on its statistical distribution are considered.
Chapter 4 is on Bayesian estimation and classification. In this chapter
the estimation problem is formulated within the general framework of
Bayesian inference. The chapter includes Bayesian theory, classical
estimators, the estimate–maximise method, the Cramér–Rao bound on the
minimum−variance estimate, Bayesian classification, and the modelling of
the space of a random signal. This chapter provides a number of examples
on Bayesian estimation of signals observed in noise.Chapter 5 considers hidden Markov models (HMMs) for nonstationary
signals. The chapter begins with an introduction to the modelling
of non-stationary signals and then concentrates on the theory and
applications of hidden Markov models. The hidden Markov model is
introduced as a Bayesian model, and methods of training HMMs and using
them for decoding and classification are considered. The chapter also
includes the application of HMMs in noise reduction.
Chapter 6 considers Wiener Filters. The least square error filter is
formulated first through minimisation of the expectation of the squared
error function over the space of the error signal. Then a block-signal
formulation of Wiener filters and a vector space interpretation of Wiener
filters are considered. The frequency response of the Wiener filter is
derived through minimisation of mean square error in the frequency
domain. Some applications of the Wiener filter are considered, and a case
study of the Wiener filter for removal of additive noise provides useful
insight into the operation of the filter.
Chapter 7 considers adaptive filters. The chapter begins with the statespace
equation for Kalman filters. The optimal filter coefficients are
derived using the principle of orthogonality of the innovation signal. The
recursive least squared (RLS) filter, which is an exact sample-adaptive
implementation of the Wiener filter, is derived in this chapter. Then the
steepest−descent search method for the optimal filter is introduced. The
chapter concludes with a study of the LMS adaptive filters.
Chapter 8 considers linear prediction and sub-band linear prediction
models. Forward prediction, backward prediction and lattice predictors are
studied. This chapter introduces a modified predictor for the modelling of
the short−term and the pitch period correlation structures. A maximum a
posteriori (MAP) estimate of a predictor model that includes the prior
probability density function of the predictor is introduced. This chapter
concludes with the application of linear prediction in signal restoration.
Chapter 9 considers frequency analysis and power spectrum estimation.
The chapter begins with an introduction to the Fourier transform, and the
role of the power spectrum in identification of patterns and structures in a
signal process. The chapter considers non−parametric spectral estimation,
model-based spectral estimation, the maximum entropy method, and high−
resolution spectral estimation based on eigenanalysis.
Chapter 10 considers interpolation of a sequence of unknown samples.
This chapter begins with a study of the ideal interpolation of a band-limited
signal, a simple model for the effects of a number of missing samples, and
the factors that affect interpolation. Interpolators are divided into twocategories: polynomial and statistical interpolators. A general form of
polynomial interpolation as well as its special forms (Lagrange, Newton,
Hermite and cubic spline interpolators) are considered. Statistical
interpolators in this chapter include maximum a posteriori interpolation,
least squared error interpolation based on an autoregressive model,
time−frequency interpolation, and interpolation through search of an
adaptive codebook for the best signal.
Chapter 11 considers spectral subtraction. A general form of spectral
subtraction is formulated and the processing distortions that result form
spectral subtraction are considered. The effects of processing-distortions on
the distribution of a signal are illustrated. The chapter considers methods
for removal of the distortions and also non-linear methods of spectral
subtraction. This chapter concludes with an implementation of spectral
subtraction for signal restoration.
Chapters 12 and 13 cover the modelling, detection and removal of
impulsive noise and transient noise pulses. In Chapter 12, impulsive noise
is modelled as a binary−state non-stationary process and several stochastic
models for impulsive noise are considered. For removal of impulsive noise,
median filters and a method based on a linear prediction model of the signal
process are considered. The materials in Chapter 13 closely follow Chapter
12. In Chapter 13, a template-based method, an HMM-based method and an
AR model-based method for removal of transient noise are considered.
Chapter 14 covers echo cancellation. The chapter begins with an
introduction to telephone line echoes, and considers line echo suppression
and adaptive line echo cancellation. Then the problem of acoustic echoes
and acoustic coupling between loudspeaker and microphone systems are
considered. The chapter concludes with a study of a sub-band echo
cancellation system
Chapter 15 is on blind deconvolution and channel equalisation. This
chapter begins with an introduction to channel distortion models and the
ideal channel equaliser. Then the Wiener equaliser, blind equalisation using
the channel input power spectrum, blind deconvolution based on linear
predictive models, Bayesian channel equalisation, and blind equalisation
for digital communication channels are considered. The chapter concludes
with equalisation of maximum phase channels using higher-order statistics.

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