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Grey noise spectrum The result is that grey noise contains all frequencies with equal loudness , as opposed to white noise , which contains all frequencies with equal energy . The difference between the two is the result of psychoacoustics , more specifically the fact that the human hearing is more sensitive to some frequencies than others.
Noise that has a frequency spectrum of predominantly zero power level over all frequencies except for a few narrow bands or spikes. Note: An example of black noise in a facsimile transmission system is the spectrum that might be obtained when scanning a black area in which there are a few random white spots. Thus, in the time domain, a few ...
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 .
The Coloured Book protocols were a set of communication protocols for computer networks developed in the United Kingdom in the 1970s. These protocols were designed to enable communication and data exchange between different computer systems and networks.
Thermal noise is approximately white, meaning that its power spectral density is nearly equal throughout the frequency spectrum. The amplitude of the signal has very nearly a Gaussian probability density function. A communication system affected by thermal noise is often modelled as an additive white Gaussian noise (AWGN) channel.
The grey noise definition contradicts the notion of white noise being perceived as white. Furthermore, its energy distribution should, according to how it is defined here, be a function of the absolute sound pressure level.
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In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1] [2] In other words, the values that the noise can take are Gaussian-distributed.