Digital Signals Theory

Brian McFee

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Hardback

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English

Chapman & Hall/CRC
16 October 2023

Technical design; Environmental science, engineering & technology; Computing: general; Artificial intelligence; Audio processing

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.

By: Brian McFee
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 760g
ISBN: 9781032207148
ISBN 10: 1032207140
Pages: 259
Publication Date: 16 October 2023
Audience: College/higher education , Adult education , Professional and scholarly , Primary , Tertiary & Higher Education
Format: Hardback
Publisher's Status: Active

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.

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.