Title:
A method for developing an improved mapping model for data sonification
A method for developing an improved mapping model for data sonification
Author(s)
Worrall, David
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Abstract
The unreliable detection of information in sonifications of
multivariate data that employ parameter mapping is generally
thought to be the result of the co-dependency of
psychoacoustic dimensions. The method described here is
aimed at discovering whether the perceptual accuracy of such
information can be improved by rendering the sonification of
the data with a mapping model influenced by the gestural
metrics of performing musicians playing notated versions of
the data. Conceptually, the Gesture-Encoded Sound Model
(GESM) is a means of transducing multivariate datasets to
sound synthesis and control parameters in such as way as to
make the information in those datasets available to general
listeners in a more perceptually coherent and stable way than
is currently the case. The approach renders to sound a
datastream not only using observable quantities (inverse
transforms of known psychoacoustic principles), but latent
variables of a Dynamic Bayesian Network trained with
gestures of the physical body movements of performing
musicians and hypotheses concerning other observable
quantities of their coincident acoustic spectra. If successful,
such a model should significantly broaden the applicability of
data sonification as a perceptualisation technique.
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Date Issued
2011-06
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Proceedings