Pink noise or ^{1}⁄_{f} noise (sometimes also called flicker noise) is a signal or process with a frequency spectrum such that the power spectral density (energy or power per Hz) is inversely proportional to the frequency of the signal. In pink noise, each octave (halving/doubling in frequency) carries an equal amount of noise power. The name arises from the pink appearance of visible light with this power spectrum.^{[1]}
Within the scientific literature the term pink noise is sometimes used a little more loosely to refer to any noise with a power spectral density of the form

S(f) \propto \frac{1}{f^\alpha}
where f is frequency and 0 < α < 2, with exponent α usually close to 1. These pinklike noises occur widely in nature and are a source of considerable interest in many fields. The distinction between the noises with α near 1 and those with a broad range of α approximately corresponds to a much more basic distinction. The former (narrow sense) generally come from condensed matter systems in quasiequilibrium, as discussed below.^{[2]} The latter (broader sense) generally correspond to a wide range of nonequilibrium driven dynamical systems.
The term flicker noise is sometimes used to refer to pink noise, although this is more properly applied only to its occurrence in electronic devices due to a direct current. Mandelbrot and Van Ness proposed the name fractional noise (sometimes since called fractal noise) to emphasize that the exponent of the spectrum could take noninteger values and be closely related to fractional Brownian motion, but the term is very rarely used.
Contents

Description 1

Generalization to more than one dimension 2

Occurrence 3

See also 4

Footnotes 5

References 6

External links 7
Description
Spectrum of a pink noise approximation on a loglog plot. Power density falls off at 10 dB/decade of frequency.
There is equal energy in all octaves (or similar log bundles) of frequency. In terms of power at a constant bandwidth, pink noise falls off at 3 dB per octave. At high enough frequencies pink noise is never dominant. (White noise is equal energy per hertz.)
The human auditory system, which processes frequencies in a roughly logarithmic fashion approximated by the Bark scale, does not perceive frequency octaves with equal sensitivity; signals in the 1–4kHz octave sound loudest, and the loudness of other frequencies drops increasingly, depending both on the distance from the peaksensitivity area and on the level. However, humans still differentiate between white noise and pink noise with ease.
Graphic equalizers also divide signals into bands logarithmically and report power by octaves; audio engineers put pink noise through a system to test whether it has a flat frequency response in the spectrum of interest. Systems that do not have a flat response can be equalized by creating an inverse filter using a graphic equalizer. Because pink noise has a tendency to occur in natural physical systems it is often useful in audio production. Pink noise can be processed, filtered, and/or effects can be added to produce desired sounds. Pink noise generators are commercially available.
One parameter of noise, the peak versus average energy contents, or crest factor, is important for testing purposes, such as for audio power amplifier and loudspeaker capabilities because the signal power is a direct function of the crest factor. Various crest factors of pink noise can be used in simulations of various levels of dynamic range compression in music signals. On some digital pink noise generators the crest factor can be specified.


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Generalization to more than one dimension
The spectrum of pink noise is ^{1}⁄_{f} only for onedimensional signals. For twodimensional signals (e.g., images) the spectrum is reciprocal to f ^{2}. In general, in an ndimensional system, the spectrum is reciprocal to f ^{n}. For higherdimensional signals it is still true (by definition) that each octave carries an equal amount of noise power. The frequency spectrum of twodimensional signals, for instance, is also twodimensional, and the area covered by succeeding octaves is four times as large.
Occurrence
Pink noise occurs in many physical, biological and economic systems. Some researchers describe it as being ubiquitous.^{[3]} In physical systems, it is present in some meteorological data series, the electromagnetic radiation output of some astronomical bodies, and in almost all electronic devices (referred to as flicker noise). In biological systems, it is present in, for example, heart beat rhythms, neural activity, and the statistics of DNA sequences, as a generalized pattern.^{[4]}
In financial systems, it is often referred to as a longterm memory effect. Also, it describes the statistical structure of many natural images (images from the natural environment).^{[3]} Recently, pink noise has also been successfully applied to the modeling of mental states in psychology,^{[5]} and used to explain stylistic variations in music from different cultures and historic periods.^{[6]} Richard F. Voss and J. Clarke claim that almost all musical melodies, when each successive note is plotted on a scale of pitches, will tend towards a pink noise spectrum.^{[7]} Similarly, a generally pink distribution pattern has been observed in film shot length by researcher James E. Cutting of Cornell University, in the study of 150 popular movies released from 1935 to 2005.^{[8]}
Although sandpile models, there are no simple mathematical models to create pink noise. It is usually generated by filtering white noise.^{[7]}^{[9]}^{[10]}
There are many theories of the origin of pink noise. Some theories attempt to be universal, while others are applicable to only a certain type of material, such as semiconductors. Universal theories of pink noise remain a matter of current research interest.
A hypothesis has been proposed to explain the genesis of pink noise on the basis of a mathematical convergence theorem related to the central limit theorem of statistics.^{[11]} The Tweedie convergence theorem^{[12]} describes the convergence of certain statistical processes towards a family of statistical models known as the Tweedie distributions. These distributions are characterized by a variance to mean power law, that have been variously identified in the ecological literature as Taylor's law^{[13]} and in the physics literature as fluctuation scaling.^{[14]} When this variance to mean power law is demonstrated by the method of expanding enumerative bins this implies the presence of pink noise, and vice versa.^{[11]} Both of these effects can be shown to be the consequence of mathematical convergence such as how certain kinds of data will converge towards the normal distribution under the central limit theorem.
Electronic devices
A pioneering researcher in this field was Aldert van der Ziel.^{[15]}
In electronics, white noise will be stronger than pink noise (flicker noise) above some corner frequency. There is no known lower bound to pink noise in electronics. Measurements made down to 10^{−6} Hz (taking several weeks) have not shown a ceasing of pinknoise behaviour.
A pink noise source is sometimes included on analog synthesizers (although a white noise source is more common), both as a useful audio sound source for further processing, and also as a source of random control voltages for controlling other parts of the synthesizer.
The principal sources of pink noise in electronic devices are almost invariably the slow fluctuations of properties of the condensedmatter materials of the devices. In many cases the specific sources of the fluctuations are known. These include fluctuating configurations of defects in metals, fluctuating occupancies of traps in semiconductors, and fluctuating domain structures in magnetic materials.^{[2]}^{[16]} The explanation for the approximately pink spectral form turns out to be relatively trivial, usually coming from a distribution of kinetic activation energies of the fluctuating processes.^{[17]} Since the frequency range of the typical noise experiment (e.g., 1 Hz — 1 kHz) is low compared with typical microscopic "attempt frequencies" (e.g., 10^{14} Hz), the exponential factors in the Arrhenius equation for the rates are large. Relatively small spreads in the activation energies appearing in these exponents then result in large spreads of characteristic rates. In the simplest toy case, a flat distribution of activation energies gives exactly a pink spectrum, because \textstyle \frac{d}{df}\ln f = \frac{1}{f}.
See also

^ Downey, Allen (2012). Think Complexity. O'Reilly Media. p. 79.

^ ^{a} ^{b} Kogan, Shulim (1996). Electronic Noise and Fluctuations in Solids. [Cambridge University Press].

^ ^{a} ^{b} Bak, P. and Tang, C. and Wiesenfeld, K. (1987). "SelfOrganized Criticality: An Explanation of 1/ƒ Noise".

^ Josephson, Brian D. (1995). "A transhuman source of music?" in (P. Pylkkänen and P. Pylkkö, eds.) New Directions in Cognitive Science, Finnish Artificial Intelligence Society, Helsinki; pp. 280–285.

^ Van Orden, G.C. and Holden, J.G. and Turvey, M.T. (2003). "Selforganization of cognitive performance". Journal of Experimental Psychology: general 132 (3): 331–350.

^ Pareyon, G. (2011). On Musical SelfSimilarity, International Semiotics Institute & University of Helsinki. "On Musical SelfSimilarity".

^ ^{a} ^{b} Noise in Mangenerated Images and Sound

^ Anger, Natalie (March 1, 2010). "Bringing New Understanding to the Director's Cut". The New York Times. Retrieved on March 3, 2010. See also original study

^ DSP Generation of Pink Noise

^ [1]

^ ^{a} ^{b} Kendal WS & Jørgensen BR (2011) Tweedie convergence: a mathematical basis for Taylor's power law, 1/f noise and multifractality. Phys. Rev E 84, 066120

^ Jørgensen, B; Martinez, JR & Tsao, M (1994). "Asymptotic behaviour of the variance function". Scand J Statist 21: 223–243.

^ Taylor LR (1961) Aggregation, variance and the mean. Nature 189, 732–735

^ Eisler Z, Bartos I & Kertesz (2008) Fluctuation scaling in complex systems: Taylor’s law and beyond. Adv Phys 57, 89–142

^ Aldert van der Ziel, (1954), Noise, Prentice–Hall

^ Weissman, M. B. (1988). Noise and other slow nonexponential kinetics in condensed matter"ƒ"1/.

^ Dutta, P. and Horn, P. M. (1981). "Lowfrequency fluctuations in solids: 1/f noise".
References

Bak, P. and Tang, C. and Wiesenfeld, K. (1987). "SelfOrganized Criticality: An Explanation of 1/ƒ Noise".

Dutta, P. and Horn, P. M. (1981). "Lowfrequency fluctuations in solids: 1/ƒ noise".

Field, D. J. (1987). "Relations Between the Statistics of Natural Images and the Response Profiles of Cortical Cells" (PDF). Journal of the

Gisiger, T. (2001). "Scale invariance in biology: coincidence or footprint of a universal mechanism?".

Johnson, J. B. (1925). "The Schottky effect in low frequency circuits".

Kogan, Shulim (1996). Electronic Noise and Fluctuations in Solids. [Cambridge University Press].

Press, W. H. (1978). "Flicker noises in astronomy and elsewhere" (PDF). Comments on Astrophysics 7: 103–119.



Keshner, M. S. (1982). "1/ƒ noise".

Li, W. (1996–present). noise"ƒ"A bibliography on 1/.


A. Chorti and M. Brookes (2007), "Resolving nearcarrier spectral infinities due to 1/f phase noise in oscillators", ICASSP 2007, Vol. 3, 15–20 April 2007, Pages:III–1005 — III–1008, DOI 10.1109/ICASSP.2007.366852
External links

noise^{α}f noise, or more generally, 1/fPowernoise: Matlab software for generating 1/

NoisefA Bibliography on 1/

1/f noise at Scholarpedia

White Noise Definition Vs Pink Noise
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