One of the features of these models is their ability to concretize
tonal relations in a manner allowing easy exploration through the interface.
The following three images show snapshots from a "virtual excursion"
through one such constructed space:
We are also exploring the use of neural-net models of
auditory perception and music cognition. The image at
right is a screenshot from a simple network trained
to differentiate between different classifications of
timbre. The classification occurs through a spectral
analysis (FFT) of the incoming signal. The net is
given a training set of several thousand spectra with
particular characteristics, and it rapidly learns to
differentiate between timbre-classes based on
Although the network model is rather crude, the system actually can learn to identify timbral features. Timbre remains one of the most poorly-understood areas of music theory and music cognition. Much current research is being done in an attempt to understand more completely how we "make sense" of complex and evolving sounds.
This particular application not only demonstrates
the type of research we are doing towards the
goal of creating new sounds, it also exmplifies
a method for "auralizing" a particular model --
it is very easy to hear how different evolutionary
factors affect the a-life model; where these
effects are much more diffcult to apprehend through
other perceptual modalities.