Concept

# Noise (signal processing)

Summary
In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory. Signal processing noise can be classified by its statistical properties (sometimes called the "color" of the noise) and by how it modifies the intended signal: Additive noise, gets added to the intended signal White noise Additive white Gaussian noise Black noise Gaussian noise Pink noise or flicker noise, with 1/f power spectrum Brownian noise, with 1/f2 power spectrum Contaminated Gaussian noise, whose PDF is a linear mixture of Gaussian PDFs Power-law noise Cauchy noise Multiplicative noise, multiplies or modulates the intended signal Quantization error, due to conversion from continuous to discrete values Poisson noise, typical of signals that are rates of discrete events Shot noise, e.g.