Sound and data parameters
Understanding the variety of ways data can be represented as sound.
Last updated
Understanding the variety of ways data can be represented as sound.
Last updated
When creating a data sonification, there are many choices to be made regarding what aspects of the data are converted to sound, and what characteristics of sound are used in this representation.
In , dimensions of data are matched to dimensions of sound. Check out Matt Russo's , and Jordan Wirfs-Brock's .
Dimensions of data that can be converted to sound
Mapping function / data selection Which variables of the data set are getting converted to sound?
Polarity What is the direction of relationship between your data values and audio parameters? (For example, are larger numbers matched with higher pitch/volume? Or it is vice versa?)
Range The span of audio values to which the data is transferred, such as a range of musical notes or volume.
Scaling Mathematical relationship between data and audio parameters, such as linear or logarithmic.
Dimensions of sound to represent the data
Pitch Note frequency (Hz). In other words, "highness" vs. "lowness."
Timbre / texture The quality of a sound or tone; the distinct "color" of a sound.
Loudness / volume = Perceived level of sound that is heard by the listener, related to the magnitude of a sound.
Tempo Rhythm and cadence with which sound is played.
Duration The length of time the sound lasts.
Panning / stereo image The position of audio from left to right speaker or headphone.
