Digital signal processing and machine learning require digital data which can be processed by algorithms on computer. However, most of the real-world signals that we observe are real numbers, occurring at real time values. This means that it is impossible in practice to store these signals on a computer and we must find some approximate signal representation which is amenable to finite, digital storage. This chapter describes the main methods which are used in practice to solve this representation problem.
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Discrete signals: sampling, quantization and coding
Semantic Scholar · Computer Science · 2019
Abstract
Digital signal processing and machine learning require digital data which can be processed by algorithms on computer. However, most of the real-world signals that we observe are real numbers, occurring at real time values. This means that it is impossible in practice to store these signals on a computer and we must find some approximate signal representation which is amenable to finite, digital storage. This chapter describes the main methods which are used in practice to solve this representation problem.