For a long time I’ve wanted to understand what differentiates different types of noises. I had often heard terms such as “white noise” and “brown noise” but I never knew what they meant.
Noise comes in many flavor. Different types of noises have different distributions of frequency bands. For example, white noise, which is the most common variety of noise, has an even distribution of all frequencies. This means that at any moment white noise is as likely to contain frequencies at 1,000Hz as it is to contain frequencies at 20Hz or 12,000Hz.
The likelyhood of finding a specific frequency band within a noise signal at any moment in time is usually referred to as the energy of that specific band. Therefore, we could say that all frequency bands feature equal energy in white noise. The noise~ object in Pure Data generates white noise when it is not modified by low-pass, high-bass, and band-pass filters.
Noises that feature distribution of frequencies which are limited to specific ranges within the overall spectrum are usually referred to using color analogies. There are no hard and fast rules about mapping color names to noise, but the analogy to light is generally observed so that blue noise has a higher frequency band than yellow noise which has a higher frequency band than red noise and so forth.
Here is an overview of the approximate color spectrum for different types of noise:
- Black Noise: 0Hz – 50Hz; explosion rumble, near infrasonic, sub bass
- Brown Noise: 50Hz – 250Hz; thunder, distant plane or rocket, large moving objects
- Red Noise: 250Hz – 500Hz; airplane cabin, train or car interior
- Orange Noise: 500Hz – 1kHz; river, city ambiance
- Yellow Noise: 1kHz – 3kHz; rain, distant waterfall
- Green Noise: 3kHz – 7kHz; bacon frying, acid on concrete
- Blue Noise: 7kHz – 15kHz; light breeze, aerosol, hihats
- Violet Noise: 15kHz – 30kHz; gas escaping, angry snake
Some types of noise are characterized by other features. For example, pink noise has an equal distribution of energy across all octaves (but not across all individual frequencies). This means that there is the same likelyhood of finding a frequency in one octave interval (such as 500Hz to 1000Hz) as in the octave interval above it (such as 1000Hz to 2000Hz) or that one above that (such as 2kHz to 4kHz).
Other types of noise include Gaussian noise, which features a bell-shaped distribution curve; and power law noise, which features a distribution that looks like a steeply descending curve with a long tail.
Below is a screenshot of the pure data patch that I developed to investigate the sound of these different types of noises (you can download the patch here). I got most of this information from a tutorial featured on the obiwannabe website. Here is a link to the tutorial that featured this pd patch.
Download the patch here and play around with it on your own machine.