Introduction To Digital Filtering

Introduction to sampling and z-transforms. General characteristics of digital filter. Synthesis of digital filters from continuous filter data. Direct synthesis of digital filters. Filters with finite duration impulse responses. Fourier transform methods. Frequency sampling filters. Frequency sampling filters with interger multipliers. Quantization effects in digital filters. Optimization techniques in digital filter design.

A digital filter can be pictured as a "black box" that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. This book is a gentle introduction to digital filters, including mathematical theory, illustrative examples, some audio applications, and useful software starting points. The theory treatment begins at the high-school level, and covers fundamental concepts in linear systems theory and digital filter analysis. Various "small" digital filters are analyzed as examples, particularly those commonly used in audio applications. Matlab programming examples are emphasized for illustrating the use and development of digital filters in practice.

Introduction to sampling and z-transforms. General characteristics of digital filter. Synthesis of digital filters from continuous filter data. Direct synthesis of digital filters. Filters with finite duration impulse responses. Fourier transform methods. Frequency sampling filters. Frequency sampling filters with interger multipliers. Quantization effects in digital filters. Optimization techniques in digital filter design.

A practical and accessible guide to understanding digital signal processing Introduction to Digital Signal Processing and Filter Design was developed and fine-tuned from the author's twenty-five years of experience teaching classes in digital signal processing. Following a step-by-step approach, students and professionals quickly master the fundamental concepts and applications of discrete-time signals and systems as well as the synthesis of these systems to meet specifications in the time and frequency domains. Striking the right balance between mathematical derivations and theory, the book features: * Discrete-time signals and systems * Linear difference equations * Solutions by recursive algorithms * Convolution * Time and frequency domain analysis * Discrete Fourier series * Design of FIR and IIR filters * Practical methods for hardware implementation A unique feature of this book is a complete chapter on the use of a MATLAB(r) tool, known as the FDA (Filter Design and Analysis) tool, to investigate the effect of finite word length and different formats of quantization, different realization structures, and different methods for filter design. This chapter contains material of practical importance that is not found in many books used in academic courses. It introduces students in digital signal processing to what they need to know to design digital systems using DSP chips currently available from industry. With its unique, classroom-tested approach, Introduction to Digital Signal Processing and Filter Design is the ideal text for students in electrical and electronic engineering, computer science, and applied mathematics, and an accessible introduction or refresher for engineers and scientists in the field.

The order in which the subject matter is presented enables students to make an easy transition from continuous signals and systems to their discrete-time counterparts. A general introduction to terminology and a description of digital filters is followed by a review of continuous filter design. Subsequent chapters deal with sampling theorem and the z-transform; design of recursive digital filters; finite-impulse response and nonrecursive filters; basic concepts in probability theory and random processes; and the methods of design and analysis of the Kalman filter. Contains worked analytical examples, diagrams and problem sets.

Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.

Digital signals occur in an increasing number of applications: in telephone communications; in radio, television, and stereo sound systems; and in spacecraft transmissions, to name just a few. This introductory text examines digital filtering, the processes of smoothing, predicting, differentiating, integrating, and separating signals, as well as the removal of noise from a signal. The processes bear particular relevance to computer applications, one of the focuses of this book. Readers will find Hamming's analysis accessible and engaging, in recognition of the fact that many people with the strongest need for an understanding of digital filtering do not have a strong background in mathematics or electrical engineering. Thus, this book assumes only a knowledge of calculus and a smattering of statistics (reviewed in the text). Adopting the simplest, most direct mathematical tools, the author concentrates on linear signal processing; the main exceptions are the examination of round-off effects and a brief mention of Kalman filters. This updated edition includes more material on the z-transform as well as additional examples and exercises for further reinforcement of each chapter's content. The result is an accessible, highly useful resource for the broad range of people working in the field of digital signal processing.