Introduction to Signal Processing and Machine Learning

Abstract

This chapter introduces this book and presents a brief overview of some key aspects and terms related to signal processing and machine learning. Though intended for non-specialists, it includes some technical details and introductory background information required for the rest of the chapters. The chapter starts with the basic definitions and concepts of signal processing, stressing on its applications in the modern era. The challenges related to traditional digital signal processing techniques as well as the immediate global need to deal with enormous data are emphasized, elaborating on the need to use machine learning algorithms and SPML (signal processing and machine learning) techniques which is the latest advancement in this field. Several basic concepts of signal classification, distance metrics, discrete signal representation, sampling, quantization, change of basis, and the importance of the analysis of time and frequency domains are discussed to review the fundamentals of signal processing. The chapter proceeds with a discussion on the benefits of distance-based signal classification, nearest neighbor classifier, and Hilbert space. Some basic terminologies of machine learning are also covered. The chapter then concludes by highlighting the benefits of SPML and how signal processing links seamlessly with machine learning to develop essential solutions to real-world problems.