**Digital Signal Processing** (DSP) comprises the techniques and algorithms for transforming, filtering and representing digital signals (DSP is a subfield of the more general Signal Processing topic).

**Continuous and Digital Signal Processing**

A signal is a measurement of a process, an observation of the behavior of some system. Numerically, a signal is a time-varying or spatial-varying quantity (in the following, for simplicity, we’ll assume that the independent variable is time *t*). Some physical signals, such as speech and image are **continuous** in time. For instance, the speech signal is a continuously varying acoustic pressure wave. Sometimes, continuous signals *x(t)* are referred to as continuous or analog waveforms (continuous and analog are typically interchangeable terms albeit analog is a kind of absolute term, and we will not be using it in the following). Continuous signals vary at an uncountable infinite number of times. On its side, digital processing units can only handle sequences of numbers, i.e., they are discrete devices. In order to harness the benefits of digital processing units, continuous signals have to be first discretized (sampled). After sampling, we get a **digital** signal, which we might use as a representation of the original continuous signal. This sampling process is performed by a *Continuous-to-Discrete* (C/D) converter.

Summarizing, **sampling** is the process by which a digital representation of a continuous time signal is obtained. Basically, during sampling we select a finite number of data points (in a finite time interval) to represent the infinite amount of data that the continuous signal contains (within the same interval). If sampling is periodic, we sample *x(t)* at uniformly spaced time instants. Sampling is by no means a trivial issue, and we have to be careful in selecting the discrete data values… how well does this discrete sequence represent the continuous signal?.

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