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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.〔 Vyacheslav Tuzlukov (2010), ''Signal Processing Noise'', Electrical Engineering and Applied Signal Processing Series, CRC Press. 688 pages. ISBN 9781420041118 〕 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, namely the Nyquist–Shannon sampling theorem. == Types of noise == 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 * * Pink noise * * Black noise * * Gaussian noise * * Flicker noise, with 1/''f'' power spectrum * * Brown noise or Brownian noise, with 1/''f''2 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. caused by static electricity discharge * Transient noise, a short pulse followed by decaying oscillations * Burst noise, powerful but only during short intervals * Phase noise, random time shifts in a signal 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Noise (signal processing)」の詳細全文を読む スポンサード リンク
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