Overview

Where most introductory texts to the field of digital signal processing assume a degree of technical knowledge, this class-tested textbook provides a comprehensive introduction to the fundamentals of digital signal processing in a way that is accessible to all. Beginning from the first principles, readers will learn how signals are acquired, represented, analyzed and transformed by digital computers. Specific attention is given to digital sampling, discrete Fourier analysis and linear filtering in the time and frequency domains. All concepts are introduced practically and theoretically, combining intuitive illustrations, mathematical derivations and software implementations written in the Python programming language. Practical exercises are included at the end of each chapter to test reader knowledge. Written in a clear and accessible style, Digital Signals Theory is particularly aimed at students and general readers interested in audio and digital signal processing, but who may not have extensive mathematical or engineering training.

Full Product Details

Author:   Brian McFee
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.520kg
ISBN:  

9781032200507

ISBN 10:   1032200502
Pages:   259
Publication Date:   16 October 2023
Audience:   College/higher education ,  Adult education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Further / Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Signals. Digital Sampling. Convolution. The Discrete Fourier Transform. Properties of the DFT. DFT Invertibility. Fast Fourier Transform. Time Frequency Representation. Frequency Domain Convolution. Infinite Impulse Response Filters. Analyzing IIR filters. Appendix.

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Author Information

Brian McFee is Assistant Professor of Music Technology and Data Science at New York University. He develops machine learning tools to analyze music and multimedia data. This includes recommender systems, image and audio analysis, similarity learning, cross-modal feature integration, and automatic annotation.

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