An in-depth treatment of algorithms and standards for perceptual coding of high-fidelity audio, this self-contained reference surveys and addresses all aspects of the field. Coverage includes signal processing and perceptual (psychoacoustic) fundamentals, details on relevant research and signal models, details on standardization and applications, and details on performance measures and perceptual measurement systems. It includes a comprehensive bibliography with over 600 references, computer exercises, and MATLAB-based projects for use in EE multimedia, computer science, and DSP courses. An ftp site containing supplementary material such as wave files, MATLAB programs and workspaces for the students to solve some of the numerical problems and computer exercises in the book can be found at ftp://ftp.wiley.com/public/sci_tech_med/audio_signal

Audio Signal Processing and Coding

Computers & Technology

Spanias, Andreas

Painter, Ted

Atti, Venkatraman

Networking & Cloud Computing

Wiley-Interscience

This book describes several modules of the Code Excited Linear Prediction (CELP) algorithm. The authors use the Federal Standard-1016 CELP MATLAB(r) software to describe in detail several functions and parameter computations associated with analysis-by-synthesis linear prediction. The book begins with a description of the basics of linear prediction followed by an overview of the FS-1016 CELP algorithm. Subsequent chapters describe the various modules of the CELP algorithm in detail. In each chapter, an overall functional description of CELP modules is provided along with detailed illustrations of their MATLAB(r) implementation. Several code examples and plots are provided to highlight some of the key CELP concepts. Table of Contents: Introduction to Linear Predictive Coding / Autocorrelation Analysis and Linear Prediction / Line Spectral Frequency Computation / Spectral Distortion / The Codebook Search / The FS-1016 Decoder

MATLAB Software for the Code Excited Linear Prediction Algorithm: The Federal Standard-1016 (Synthesis Lectures on Algorithms and Software in Engineering, 3)

Science & Math

Ramamurthy, Karthikeyan

Technology

Morgan and Claypool Publishers

The MPEG-1 Layer III (MP3) algorithm is one of the most successful audio formats for consumer audio storage and for transfer and playback of music on digital audio players. The MP3 compression standard along with the AAC (Advanced Audio Coding) algorithm are associated with the most successful music players of the last decade. This book describes the fundamentals and the MATLAB implementation details of the MP3 algorithm. Several of the tedious processes in MP3 are supported by demonstrations using MATLAB software. The book presents the theoretical concepts and algorithms used in the MP3 standard. The implementation details and simulations with MATLAB complement the theoretical principles. The extensive list of references enables the reader to perform a more detailed study on specific aspects of the algorithm and gain exposure to advancements in perceptual coding. 
 Table of Contents: Introduction / Analysis Subband Filter Bank / Psychoacoustic Model II / MDCT / Bit Allocation, Quantization and Coding / Decoder

Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB (Synthesis Lectures on Algorithms and Software in Engineering)

Engineering & Transportation

Thiagarajan, Jayaraman J.

Engineering

Morgan & Claypool Publishers

The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript. Table of Contents: Acknowledgments / Introduction / The Estimation Problem / The Kalman Filter / Extended and Decentralized Kalman Filtering / Conclusion / Notation / Bibliography / Authors' Biographies

An Introduction to Kalman Filtering with MATLAB Examples (Synthesis Lectures on Signal Processing)

Kovvali, Narayan

Banavar, Mahesh

Programming

Autonomous vehicles use global navigation satellite systems (GNSS) to provide a position within a few centimeters of truth. Centimeter positioning requires accurate measurement of each satellite's direct path propagation time. Multipath corrupts the propagation time estimate by creating a time-varying bias. A GNSS receiver model is developed and the effects of multipath are investigated. MATLABtm code is provided to enable readers to run simple GNSS receiver simulations. More specifically, GNSS signal models are presented and multipath mitigation techniques are described for various multipath conditions. Appendices are included in the booklet to derive some of the basics on early minus late code synchronization methods. Details on the numerically controlled oscillator and its properties are also given in the appendix.

Multipath Effects in GPS Receivers: A Primer (Synthesis Lectures on Communications)

Zhang, Xue

Steven Miller

Earth Sciences

Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications)

Tepedelenlioglu, Cihan

Zhang, Sai

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.

Reconstruction-Free Compressive Vision for Surveillance Applications (Synthesis Lectures on Signal Processing)

Braun, Henry

Turaga, Pavan

Computer Science

Narrowband Direction of Arrival Estimation for Antenna Arrays (Synthesis Lectures on Antennas)

Foutz, Jeffrey

Springer

Positive Unlabeled Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Jaskie, Kristen

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