Data sonification and the
sciences
Data sonification
is the representation of data by means of sound signals, so
it is the analog of scientific visualization, where we deal
with auditory instead of visual images.
Generally speaking any sonification procedure is a
mathematical mapping from a certain data set (numbers,
strings, images, ...) to a sound string. Data sonification
is currently used in several fields, for different
purposes: science and engineering, education and training,
since it provides a quick and effective data analysis and
interpretation tool. Although most data analysis techniques
are exclusively visual in nature (i.e. are based on the
possibility of looking at graphical representations), data
presentation and exploration systems could benefit greatly
from the addition of sonification capacities.
Because sounds can convey significant amounts of
information, sonification has the potential to increase the
bandwidth of the human/computer interface. Nevertheless,
its use in scientific computing has received limited
attention up to the last years because of the intensive
computations usually required to produce sound. Digital
audio usually deals with very high sampling rate, the
standard value for CD quality audio signals is 44100 Hz, so
to produce one second of audio data it is required to
compute 44100 values. One minute will take 60 x 44100=
2646000 calculated samples, just to have a snapshot of the
sonification procedure from the point of view of computing.
On the other hand, sonification could be a very precious
aid, since ear has a very high power of discrimination. As
a consequence, one can use very small frequency steps (even
smaler than a quarter tone) to take into account any tiny
variation of the data. Equally discriminating power is
available for which concern the timbre.
Finally, in the scientific domain, sound is an extremely
interesting tool (and one of the most easily suitable) to
identify regularities in the time domain, both at the level
of microstructures and on large scales. In addition to
that, sound can immediately make clear and recognizable
transitions between random states and periodic phenomena.
Most auditory processes are indeed based on the detection
of regular patterns: periodic repetitions which turn into
understandable qualities like pitch and timbres.
Moreover, sonic representations are particularly useful
when dealing with complex, high-dimensional data, or in
data monitoring tasks where it is practically impossible to
use the visual inspection. More interesting and intriguing
aspects of data sonification concern the possibility of
describing patterns or trends, through sound, which were
hardly perceivable otherwise.