The Digital Signal Processing HandbookVIJAY MADISETTI The field of digital signal processing (DSP) has spurred developments from basic theory of discrete-time signals and processing tools to diverse applications in telecommunications, speech and acoustics, radar, and video. This volume provides an accessible reference, offering theoretical and practical information to the audience of DSP users. This immense compilation outlines both introductory and specialized aspects of information-bearing signals in digital form, creating a resource relevant to the expanding needs of the engineering community. It also explores the use of computers and special-purpose digital hardware in extracting information or transforming signals in advantageous ways. Impacted areas presented include: This authoritative collaboration, written by the foremost researchers and practitioners in their fields, comprehensively presents the range of DSP: from theory to application, from algorithms to hardware. |
Contents
x | 1-8 |
Adaptive IIR Filters Geoffrey A Williamson 231 | 1-23 |
Robust Speech Processing as an Inverse Problem | 1-27 |
Finite Wordlength Effects Bruce W Bomar | 2-3 |
AnalogtoDigital Conversion Architectures Stephen Kosonocky and Peter Xiao | 3-21 |
Quantization of Discrete Time Signals Ravi P Ramachandran | 4-6 |
Fast Algorithms and Structures | 6-17 |
A Tutorial Review and a State of the | 7-7 |
Speech Coding Richard V Cox 451 | 45-1 |
TexttoSpeech Synthesis Richard Sproat and Joseph Olive 461 | 46-1 |
Speech Recognition by Machine Lawrence R Rabiner and B H Juang 471 | 47-1 |
Speaker Verification Sadaoki Furui and Aaron E Rosenberg 481 | 48-1 |
DSP Implementations of Speech Processing Kurt Baudendistel 491 | 49-1 |
Software Tools for Speech Research and Development John Shore 501 | 50-1 |
Image and Video Processing | 50-15 |
Image Processing Fundamentals Ian T Young Jan J Gerbrands | 51-51 |
Sony Systems Kenzo Akagiri M Katakura H Yamauchi E Saito | 7-43 |
323 | 8-14 |
Complexity Theory of Transforms in Signal Processing Ephraim Feig | 9-1 |
Fast Matrix Computations Andrew E Yagle | 9-10 |
Digital Filtering | 10-11 |
1138 | 11-19 |
1171 | 11-32 |
Spectrum Estimation and Modeling Petar M Djurić and Steven M Kay 141 | 1 |
From Gauss to Wiener to Kalman | 15-15 |
Introduction to Adaptive Filters Scott C Douglas 181 | 17-33 |
Adaptive Filters for Blind Equalization Zhi Ding 241 | 17-34 |
Convergence Issues in the LMS Adaptive Filter | 18-19 |
Robustness Issues in Adaptive Filtering Ali H Sayed and Markus Rupp 201 | 19-20 |
Recursive LeastSquares Adaptive Filters Ali H Sayed and Thomas Kailath 211 | 20-21 |
Signal Recovery from Partial Information Christine Podilchuk 251 | 21-25 |
Transform Domain Adaptive Filtering | 22-19 |
Fast Convolution and Filtering Ivan W Selesnick and C Sidney Burrus | 27-8 |
Inverse Problems Statistical Mechanics and Simulated Annealing | 28-1 |
Image Recovery Using the EM Algorithm Jun Zhang and Aggelos K Katsaggelos 291 | 29-1 |
Inverse Problems in Array Processing Kevin R Farrell 301 | 30-1 |
Validation Testing and Noise Modeling Jitendra K Tugnait 161 | 32-16 |
Time Frequency and Multirate Signal | 34-2 |
Introduction | 34-13 |
Wavelets and Filter Banks Cormac Herley | 34-21 |
TimeVarying AnalysisSynthesis Filter Banks Iraj Sodagar | 37-1 |
Digital Digital Audio Communications | 38-11 |
Cyclostationary Signal Analysis Georgios B Giannakis 171 | 40-17 |
Dolby AC3 Grant A Davidson 411 | 41-1 |
The Perceptual Audio Coder PAC Deepen Sinha James D Johnston | 42-1 |
Speech Processing | 43-23 |
Signal Detection and Classification Alfred Hero | 44-13 |
Still Image Compression Tor A Ramstad 521 | 51-52 |
Image and Video Restoration A Murat Tekalp 531 | 52-29 |
Video Scanning Format Conversion and Motion Estimation Gerard de Haan 541 | 54-1 |
Video Sequence Compression Osama AlShaykh Ralph Neff | 55-1 |
Digital Television KouHu Tzou 561 | 56-1 |
On Multidimensional Sampling Ton Kalker | 57-4 |
A Survey of Image Processing Software and Image Databases Stanley J Reeves 581 | 58-3 |
VLSI Architectures for Image Communications P Pirsch and W Gehrke 591 | 59-1 |
Sensor Array Processing | 59-23 |
Beamforming with Correlated Arrivals in Mobile Communications | 59-24 |
for Fast Convolution | 60-8 |
SubspaceBased Direction Finding Methods | 62-1 |
ESPRIT and ClosedForm 2D Angle Estimation with Planar Arrays | 63-1 |
A Unified Instrumental Variable Approach to Direction Finding in Colored Noise | 64-1 |
Electromagnetic VectorSensor Array Processing Arye Nehorai and Eytan Paldi 651 | 65-1 |
Subspace Tracking R D DeGroat E M Dowling and D A Linebarger 661 | 66-1 |
Determining the Number of Sources Douglas B Williams 671 | 67-1 |
Array Processing for Mobile Communications A Paulraj and C B Papadias 681 | 68-1 |
SpaceTime Adaptive Processing for Airborne Surveillance Radar Hong Wang 701 | 70-1 |
Nonlinear and Fractal Signal Processing | 70-17 |
Nonlinear Maps Steven H Isabelle and Gregory W Wornell 721 | 72-1 |
Fractal Signals Gregory W Wornell 731 | 73-1 |
| 74-27 | |
Signal Processing and Communication with Solitons Andrew C Singer 751 | 75-1 |
HigherOrder Spectral Analysis Athina P Petropulu 761 | 76-1 |
DSP Software and Hardware | 76-17 |
Channel Equalization as a Regularized Inverse Problem John F Doherty 311 | 77-31 |
135 | 78-13 |
| 78-35 | |
Other editions - View all
Common terms and phrases
Acoust adaptive filter algorithm analysis applications approach approximation array audio band bandwidth beamforming bit rate blind equalization block channel Chebyshev coders coding complex computed constraints convergence convolution decoder defined denotes digital filters Digital Signal Digital Signal Processing discrete discrete Fourier transform encoder equation error estimate example factor FIGURE filter banks filter design finite FIR filter Fourier series Fourier transform Gaussian given IEEE IEEE Trans image restoration implementation impulse response input signal inverse iterative lattice least squares length linear LMS adaptive filter masker masking matrix method minimize multiplications noise nonlinear obtained optimal orthogonal output parameters perceptual phase polynomial problem Proc properties quantization random reconstruction recursive samples sequence shown in Fig Signal Processing solution spectral spectrum Speech Signal Speech Signal Process statistical structure subband techniques theorem threshold time-varying twiddle factors values variable vector wavelet zero