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(2006 Wiley)Advanced Digital Signal Processing and Noise Reduction(3rd Edition)
Advanced Digital Signal Processing and Noise Reduction |  | 收藏 | | | 作者 | Saeed V. Vaseghi | | ＩＳＢＮ | 0-470-09494-X | | 出版日期/版本 | 2006,3rd Edition | | 格式 | ＰＤＦ Adobe Acrobat |
主题内容 | |
The applications of DSP are numerous and include multimedia technology, audio signal
processing, video signal processing, cellular mobile communication, adaptive network
management, radar systems, pattern analysis, pattern recognition, medical signal processing,
financial data forecasting, artificial intelligence, decision making systems, control systems
and information search engines.
The theory and application of signal processing is concerned with the identification,
modelling and utilisation of patterns and structures in a signal process. The observation
signals are often distorted, incomplete and noisy. Hence, noise reduction and the removal of
channel distortion and interference are important parts of a signal processing system.
Since the publication of the first edition of this book in 1996, digital signal processing
(DSP) in general and noise reduction in particular, have become even more central to the
research and development of efficient, adaptive and intelligent mobile communication and
information processing systems. The third edition of this book has been revised extensively
and improved in several ways to take account of the recent advances in theory and application
of digital signal processing. The existing chapters have been updated with new materials
added. Two new chapters have been introduced; one for speech enhancement in mobile
noisy conditions and the other for modelling and combating noise and fading in wireless
communication systems.
The aim of this book is to provide a coherent and structured presentation of the theory and
applications of statistical signal processing and noise reduction methods and is organised in
17 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,
nonstationary 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 Cramer–
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 nonstationary 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 state-space equation for
Kalman filters. The optimal filter coefficients are derived using the principle of orthogonality
of the innovation signal. The recursive least square (RLS) filter, which is an exact sampleadaptive
implementation of the Wiener filter, is derived in this chapter. Then the steepestdescent
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
nonparametric 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 two categories: polynomial and statistical interpolators. A general form of
polynomial interpolation as well as its special forms (Lagrange, Newton, Hermite and cubic
spline interpolators) is considered. Statistical interpolators in this chapter include maximum
a posteriori interpolation, least square error interpolation based on an autoregressive model,
time–frequency interpolation, and interpolation through the 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 from 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 nonlinear 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
nonstationary 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
is considered. The chapter concludes with a study of a sub-band echo cancellation system.
Chapter 15 covers 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.
Chapter 16 covers speech enhancement methods. Speech enhancement in noisy
environments improves the quality and intelligibility of speech for human communication and
increases the accuracy of automatic speech recognition systems. Noise reduction systems are
increasingly important in a range of applications such as mobile phones, hands-free phones,
teleconferencing systems and in-car cabin communication systems. This chapter provides an
overview of the main methods for single-input and multiple-input speech enhancement in
noise.
Chapter 17 covers the issue of noise in wireless communication. Noise, fading and
limited radio bandwidth are the main factors that constrain the capacity and the speed
of communication on wireless channels. Research and development of communications
systems aim to increase the spectral efficiency, defined as the data bits per second per
Hertz bandwidth of a communication channel. For improved efficiency, modern mobile
communications systems rely on signal processing methods at almost every stage from source
coding to the allocation of time bandwidth and space resources. In this chapter we consider
how communications signal processing methods are employed for improving the speed and
capacity of communications systems.
As an additional resource, this book is supported by a companion website on which
lecturers and instructors can find electronic versions of the figures. Please go to
ftp://ftp.wiley.co.uk/pub/books/vaseghi3e.
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