Sound is a collection of random signals that have certain physical characteristics that depend on the sound source. One of the physical characteristics of sound can be seen from the spectrum formed. There is a lot of noise that can be distinguished based on the spectrum character, such as White Noise, Pink Noise, Brownian Noise, Blue Noise, Violet Noise, Gray Noise, and others. In general, what is often used is White Noise, Pink Noise, and Brownian Noise both in measurement and audio testing.
Many people are very familiar with White Noise, usually, the static sound from the Air Conditioner that delivers us to sleep by disguising background noise is always considered White Noise even though technically what we hear from the Air Conditioner fan rotation is not White Noise. Many of the sounds we associate with White Noise are actually Pink Noise, Brownian Noise, Green Noise, or Blue Noise. In the world of audio engineering, there are various types of noise colors with their own unique spectrum, this is produced to give a rich impression on music arrangements, relaxation, and so forth. So, this article will explain that static noise is not always White Noise.
Here are some sound colors that are quite familiar and often discussed in the world of audio engineering:
- White Noise
The most commonly mentioned noisy color in everyday life is White Noise. White Noise is called “White” as a symbolization of a white light containing all frequencies evenly or flatly in mathematical calculations. It is said mathematically because, in reality, it is not perfectly flat. The White Noise calculation pattern is evenly distributed if it is calculated using the following equation:
So in the case of White Noise, the signal power becomes:
The resulting spectrum is in the form of a constant straight line like the following graph,
Keep in mind that the graph shown is a logarithmic function and not a linear function where the frequency range at high frequencies is wider than the frequency range at low frequency. Here is a White Noise that can be heard:
2. Pink Noise
Proportionally the pink noise spectrum is seen to decrease on a logarithmic scale but it has equal power in bands that are proportionally wide. This means that pink noise would have equal power in the frequency range from 40 to 60 Hz as in the band from 4000 to 6000 Hz. Since humans hear in such a proportional space, where a doubling of frequency (an octave) is perceived the same regardless of actual frequency (40–60 Hz is heard as the same interval and distance as 4000–6000 Hz), every octave contains the same amount of energy and thus pink noise is often used as a reference signal in audio engineering. The spectral power density, compared with white noise, decreases by 3 dB per octave (density proportional to 1/f ). For this reason, pink noise is often called “1/f noise”. Some people associate pink with red and white where pink is brighter than red and fainter than white so that it is described as a decreased spectrum with values close to a ~ 1. Mathematically, Pink Noise can be calculated using the formulation below:
The depiction of the curve produced by Pink Noise is as follows:
Pink Noise will heard like the following audio file below,
3. Brownian Noise (Red Noise)
Brownian Noise color has several names, some people call it Brown Noise, Brownian Noise, or Red Noise. Brownian was discovered by Robert Brown, the inventor of Brownian Motion (Random Walk or Drunkard’s Walk) where the Noise produced by Brownian Motion is the same as Red Noise / Brown Noise. Described as a red light that is darker than Pink and White, the spectrum formed has the characteristic of a sharp decrease that exceeds a decrease in Pink Noise (1 / f2 or a decrease of 6 dB per octave). Visually the Red Noise value is the boundary of the Pink Noise, together with the White Noise, so the spectrum curve formed is as follows:
Brownian Noise will sound like the following audio file below:
4. Blue Noise (Azure Noise)
If Red Noise and Pink Noise have a decreased character, then Blue Noise is the opposite. Blue Noise has an uphill spectrum curve characteristic that is inversely proportional to Pink Noise. Blue noise’s power density increases 3 dB per octave with increasing frequency (density proportional to f ) over a finite frequency range. In computer graphics, the term “blue noise” is sometimes used more loosely as any noise with minimal low-frequency components and no concentrated spikes in energy. This can be a good noise for dithering. Cherenkov radiation is a naturally occurring example of almost perfect blue noise, with the power density growing linearly with frequency over spectrum regions where the permeability of the index of refraction of the medium is approximately constant. The exact density spectrum is given by the Frank–Tamm formula. In this case, the finiteness of the frequency range comes from the finiteness of the range over which a material can have a refractive index greater than unity. Cherenkov radiation also appears as a bright blue color, for these reasons.
The curve produced by Blue Noise is as follows:
Blue Noise will sound like the following audio file below:
5. Violet Noise (Purple Noise)
If Blue Noise is the opposite of Pink Noise, then Violet can be categorized as the opposite of Red or Brownian Noise. This can be seen from the addition of the power density of Violet Noise which is 6 dB per octave with increasing frequency value. The proportional density of Violet Noise or often also called Purple Noise is f2 over a finite frequency range. Violet Noise is also known as differentiated white noise, due to its being the result of the differentiation of a white noise signal.
The curve produced by Violet Noise is as follows:
Violet Noise will sound like the following audio file below:
6. Grey Noise
Gray Noise is a randomized White Noise that is correlated with the same psychoacoustic noise curve or can be said to be an inverse A-weighting curve, with a specific frequency range that gives the impression or perception that this sounds equally loud at all frequencies. This is in contrast to standard white noise which has equal strength over a linear scale of frequencies but is not perceived as being equally loud due to biases in the human equal-loudness contour.
The curve produced by Grey Noise is as follows:
Grey Noise will sound like the following audio file below:
Written by:
Betabayu Santika
Acoustic Design Engineer
Geonoise Indonesia
Beta@geonoise.asia
Sources:
Pics: Noise Curves By Warrakkk – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=19274696
Hartmann, William M. Signals, sound, and sensation. Springer Science & Business Media, 2004.
“Federal Standard 1037C”. Institute for Telecommunication Sciences. Institute for Telecommunication Sciences, National Telecommunications and Information Administration (ITS-NTIA). Retrieved 16 January 2018.
Lau, Daniel Leo; Arce, Gonzalo R.; Gallagher, Neal C. (1998), “Green-noise digital halftoning”, Proceedings of the IEEE, 86 (12): 2424–42, doi:10.1109/5.735449
Joseph S. Wisniewski (7 October 1996). “Colors of noise pseudo FAQ, version 1.3”. Newsgroup: comp.dsp. Archived from the original on 30 April 2011. Retrieved 1 March 2011.
