# Brownian noise

(Redirected from Red noise)
For similar phrases, see Brown note (disambiguation).
Colors of noise
White
Pink
Red (Brownian)
Grey
Brown noise spectrum

In science, Brownian noise (), also known as Brown noise or red noise, is the kind of signal noise produced by Brownian motion, hence its alternative name of random walk noise. The term "Brown noise" comes not from the color, but after Robert Brown, the discoverer of Brownian motion.

## Explanation

The graphic representation of the sound signal mimics a Brownian pattern. Its spectral density is inversely proportional to f², meaning it has more energy at lower frequencies, even more so than pink noise. It decreases in power by 6 dB per octave (20 dB per decade) and, when heard, has a "damped" or "soft" quality compared to white and pink noise. The sound is a low roar resembling a waterfall or heavy rainfall. See also violet noise, which is a 6 dB increase per octave.

## Power spectrum

A Brownian motion, also called a Wiener process, is obtained as the integral of a white noise signal, $dW(t)$,

$W(t) = \int _{0}^{t} dW(s)$

meaning that Brownian motion is the integral of the white noise $dW(t)$ whose power spectral density is flat[1]

$S_0 = \left|\mathcal{F}\left[\frac{dW(t)}{dt}\right](\omega)\right|^2 = \text{const}$

Note that here $\mathcal{F}$ denotes the Fourier transform and $S_0$ is a constant. An important property of this transform is that the derivative of any distribution transforms as[2]

$\mathcal{F}\left[\frac{dW(t)}{dt}\right](\omega) = i \omega \mathcal{F}[W(t)](\omega)$

from which we can conclude that the power spectrum of Brownian noise is

$S(\omega)= \left|\mathcal{F}[W(t)](\omega)\right|^2= \frac{S_0}{\omega^2}$.

## Production

Brown noise can be produced by integrating white noise.[3][4] That is, whereas (digital) white noise can be produced by randomly choosing each sample independently, Brown noise can be produced by adding a random offset to each sample to obtain the next one. Note that while the first sample is random across the entire range that the sound sample can take on, the remaining offsets from there on are a tenth or there abouts, leaving room for the signal to bounce around.

### Sample

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## References

1. ^ Gardiner, C. W. Handbook of stochastic methods. Berlin: Springer Verlag.
2. ^ Barnes, J.A. and Allan, D.W. (1966). "A statistical model of flicker noise". Proceedings of the IEEE 54 (2): 176– 178. and references therein
3. ^ "Integral of White noise". 2005.
4. ^ Bourke, Paul (October 1998). "Generating noise with different power spectra laws".