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If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.
An practical introduction to computer-based signal processing techniques for scientists, engineers, researchers, instructors, and students working in academia, industry, environmental, medical, engineering, earth science, space, military, financial, agriculture, and communications, keyed to free downloadable Matlab/Octave scripts and functions and spreadsheet templates.
Introduction to Digital Signal Processing covers the basic theory and practice of digital signal processing (DSP) at an introductory level. As with all volumes in the Essential Electronics Series, this book retains the unique formula of minimal mathematics and straightforward explanations. The author has included examples throughout of the standard software design package, MATLAB and screen dumps are used widely throughout to illustrate the text. Ideal for students on degree and diploma level courses in electric and electronic engineering, 'Introduction to Digital Signal Processing' contains numerous worked examples throughout as well as further problems with solutions to enable students to work both independently and in conjunction with their course. Assumes only minimum knowledge of mathematics and electronics Concise and written in a straightforward and accessible style Packed with worked examples, exercises and self-assesment questions
Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. Multiple color illustrations are integrated in the text Includes an introduction to biomedical signals, noise characteristics, and recording techniques Basics and background for more advanced topics can be found in extensive notes and appendices A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670
This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
"This book offers an introduction to digital signal processing (DSP) with an emphasis on audio signals and computer music ... This book is designed for both technically and musically inclined readers alike--folks with a common goal of exploring digital signal processing"--Cover, p. [4].
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.
An Introduction to Digital Signal Processing is written for those who need to understand and use digital signal processing and yet do not wish to wade through a multi-semester course sequence. Using only calculus-level mathematics, this book progresses rapidly through the fundamentals to advanced topics such as iterative least squares design of IIR filters, inverse filters, power spectral estimation, and multidimensional applications--all in one concise volume. This book emphasizes both the fundamental principles and their modern computer implementation. It presents and demonstrates how simple the actual computer code is for advanced modern algorithms used in DSP. Results of these programs, which the reader can readily duplicate and use on a PC, are presented in many actual computer drawn plots. Assumes no previous knowledge of signal processing but leads up to very advanced techniquescombines exposition of fundamental principles with practical applications Includes problems with each chapter Presents in detail the appropriate computer algorithums for solving problems
This introduction to elementary signal processing connects theory and application, and bridges instruction between a book and a CD-ROM packed with video, software and more. The result is a unique, non-mathematical learning system using concepts drawn from modern brain research. Readers use the popular DasyLab metrology and control engineering program to develop applications. Processing of real signals is enabled via the sound card and the parallel port. Two hundred pre-programmed signal engineering systems and design transparencies are provided on the CD-ROM. There are numerous videos, more than 250 photos, and - most important – all "living" experiments and their results are visualized.
An Introduction to Statistical Signal Processing with Applications covers basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific applications. The book presents both detection and estimation in a clear, concise fashion and reflects recent developments and shifting emphases in the field.