Sound and light share the fundamental nature of vibration. And, even though the sounds we can hear have a much lower frequency than light that is visible to us, there is a range of sound frequencies that have corresponding consonant colors. This page delves into consonant relationships between sound and color and and provides a tool to let you explore their relationship.
However, before diving too far down this particular rabbit hole topic, please realize that the vibrations of sound and light are very different. Sound is based on vibrations of air molecules as a moving compression wave. Light (and hence color) is based on an electromagnetic wave. While “frequency” is a measure commonly used for both compression and electromagnetic waves, the two types of waves have substantial differences.
Despite these challenges, the Sound⇔Color link has been reported to be potentially useful in many contexts including the treatment of synethesia, music education, meditation practices, and therapeutic music making.
Aspects of Sound and Color
Two basic questions need to be addressed when connecting sound with color:
- What apsect of sound are we dealing with?
Aspects of sound have been connected with color include:- The vowel that is used by people who are doing vocal toning. Some vowels are “URRR…”, “AHH…”, and “EEE…”.
- The fundamental (lowest) frequency component of the sound. This is often translated into pitch based on some pitch standard and tuning system(typically a pitch standard of A=440 or A=432 and a tuning system of 12-TET or some just-intoned tuning system).
- A musical interval such as a minor third or a perfect fifth.
- How are the colors being represented or specified?
Ways of representing colors abound. The various systems, called color models, are often related to the practical aspects of displaying colors on display screens or printers. These systems often have multiple components, such as the Red-Green-Blue systems used in Cathode Ray Tube displays. Another method for specifying colors is to use the visible colors we can see in the electromagnetic spectrum — the colors of light that we see in a rainbow. In this system, it is typical to give a single number — the frequency of the electromagnetic radiation — when specifying the color. Note that not all colors can be represented in every system. Some colors produced by a computer screen do not appear in the visible light spectrum.
Connecting Sound with Color
Many approaches to the Sound-Color relationship have been explored over the centuries. These approaches are either direct or indirect relationships.
A direct relationship has some formula or mapping that translates between a pitch and a color. The mapping might be in only one direction (eg. pitch ⇒ color, where every pitch has a color, but not every color can be mapped on to a pitch), or it could be bi-directional.
An indirect relationship has some intermediate component that is mapped onto both pitches and colors, providing a sound–color link.
The image at the right — Vowel Sounds and Colors for Toning based on the Chakra System — is an example of an indirect relationship between discreet colors and vowel sounds used in toning. These are linked through the system of chakras, with each chakra assigned both a vowel sound and a discreet color.
Another aspect of the relationship is whether the colors and sounds that are linked form a discreet set of colors or sounds (for example, “red”, “green”, etc. and “C”, “Ab”, etc.) or a continuous spectrum (for example, the frequency of the light or the sound wave).
Chakra Systems
Notice that the pitches are completely independent of the musical intervals. Also note that the chakras themselves do not agree with the not agree with the prior Vowel Sounds and Colors for Toning chart. I have found that these type of discrepancies occur frequently when combining information from different sources.
Scriabin Correspondence
Alexander Scriabin (1871–1915) developed a mapping between discreet pitches and discreet colors based on his experience with synesthesia, a condition that causes one sense to be percieved as a different sense. Scriabin perceived sound as color, and developed a mapping system called “clavier à lumieères” (literally “keyboard with lights”) shown at the right.
Rather than a pattern that placed similar colors on adjacent keys, Scriabin’s system (and, presumably, his system of perception) placed similar colors on notes that were a perfect fourth and a perfect fifth apart. So, when the notes are laid out in the manner of a Circle of Fifths, the colors appear to be more systematic and continuous:
It is an open question whether such a color coding of piano keys has an effect on how piano or music theory is learned.
A Direct Relationship between Continuous Spectrums of Sound and Color
The rest of this page deals with a specific relationship between sound and color: a direct relationship between the continuous spectrum of frequencies of electromagnetic energy in the band of visible light and the pitches of sound in a continuous frequency spectrum of sound that are 40 octaves (a factor of 240 = 1,099,511,627,776) below the frequencies of visible light.
This Sound⇒Color relationship is shown in a chart and a calculator further down on this page.
The Color of Sound Chart
Some notes on this chart:
The range of this chart is somewhat wider than the calculator at the top of this page, since the visible color spectrum extends slightly more than one octave worth of frequencies. I have included both F4 and F5, which, when scaled up 40 octaves, have corresponding colors near the infrared and ultraviolet ends of the spectrum, respectively.
I have also added a column at the end that combines both F4 and F5. This is useful in cases where a cyclic display of colors is needed, to avoid a jump in color between F and F#.
The conversion from the RGB colorspace to CMY, CMYK, HSB (aka HSV), and YIQ (used in NTSC displays) were performed by Corel Draw X3.
Converting Sound to Color
The resonant color of light that is 40 octaves above an F#4 at standard pitch A4=440 has a frequency of 406.81 THz. This color has a wavelength of 736.93 nm. The equivalent RGB colors (shown above) that approximate this color of light are #750000, based on a modified version of Dan Bruton’s color approximation algorithm.
The frequency of an F#4 at standard pitch A4=440 is 369.99 Hz.
At a temperature of 72°F (22.22°C), the speed of sound is 345.31 m/sec (1132.91 ft/sec). Under these conditions, an F#4 at standard pitch A4=440 has a wavelength of 93.33 cm (36.74 inches).
The code above converts the frequency of sound to a frequency of light by doubling the sound frenency (going up one octave each time) until it reaches a frequency in the range of 400–800 THz (400,000,000,000,000 – 800,000,000,000,000 Hz).
That frequency is then converted into a wavelength of light, using the formula:
wavelength = speedOfLight / frequency
The speed of light that is used is the observed speed of light in a vacuum (299,792,458 m/sec).
I believe that this is a reasonable approach, even though we are not playing these sounds in a vacuum. The code for rendering colors (see below) is based on this same constant for speed of light; When looking at resonance, I believe that it is really the frequency of the sound and light that we want to match, not the wavelength.
Rendering Colors of Light
To display a particular wavelength of light on HTML Web pages is problematic.
Colors of light are pure frequencies that our eyes perceive as a single color. The RGB (red, green, blue) color system used by HTML and as displayed on most color monitors uses a blend of three pure light sources (a red gun, a green gun, and a blue gun, in the case of the older CRT displays) to create the impression of a single color to our eyes. In the RGB system our eyes perceive some colors that do not exist as pure colors of the spectrum, such as pink and white. These colors are blends of multiple colors from the pure spectum. The RGB color model is called an “additive” color system, because it is adding colors together to render a perceived color.
To render pure colors as RGB, I have used this table from [Bruno 2006], page 2:
| Color | Frequency | Wavelength |
|---|---|---|
| violet | 668–789 THz | 380–450 nm |
| blue | 631–668 THz | 450–475 nm |
| cyan | 606–630 THz | 476–495 nm |
| green | 526–606 THz | 495–570 nm |
| yellow | 508–526 THz | 570–590 nm |
| orange | 484–508 THz | 590–620 nm |
| red | 400–484 THz | 620–750 nm |
To approximate these colors in the RGB color model, I coded a JavaScript routine that is based on code implemented in C# by Phillip Laven that was originally from a Fortran coding of an algorithm from Dan Bruton’s Color Science Page. Note that my JavaScript version is heavily modified, so the results do not match these earlier C# and Fortran implementations, but more closely approximate this rendering done in 2010 by David Eccles (generated from code he implemented in R):
A description of this rendering is provided on the Wikimedia page for this image, and the code that generated this image is available at this code page.
If you print these color renditions, the problems of color rendition are compounded. Most printers use yet another color model, CMYK (Cyan, Magenta, Yellow, Black). Not only are the RGB colors converted to CMYK colors somewhere along the path from web page to printer, but the printer color model is a “subtractive” model — the paper starts out white and material is tossed onto the page to make it darker.
So, the bottom line is, the color renditions used on Flutopedia for color rendering of wavelengths of light are, at best, a “good try” at displaying colors, but should not be used for work that calls for a more serious treatment.
Converting RGB Colors to Color Frequencies
I’ve been asked about the possibility of converting an RGB color into a frequency of light. This poses some issues. As part of the pitch-to-color calculator, we’re mapping one number (frequency) onto three numbers (R, G, and B). But we’re not producing all possible combinations of R, G, and B … just some of them. That means that, if you pick an arbitrary R, G, and B, it might not have any frequency that (in my algorithm) would have generated that RGB.
An analogy could be made to drawing a line on a map. Each point on the line can be identified by its distance from the start of the line, and each point on the line has an [X,Y] coordinate on the map. However, each [X,Y] coordinate on the map does not have a position on the line.
Another way to look at this is … consider the rainbow. Can you find brown in it? Not really. But there’s brown in RGB space.