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Version: 1.3
31. August 2001

 to the paper
Sound and Meaning in Auditory 
Data Display
Thomas Hermann
 
Abstract
This paper focusses on the connection between human listening and
datamining.  The goal in the research field of datamining is to find
patterns, to detect hidden regularities in data. Often, high-dimensional
datasets are given which are not easily understood from pure inspection of
the table of numbers representing the data.  There are two ways to solve
the datamining problem: one is to implement perceptional capabilities in
artificial systems - this is the approach of machine learning. The other
way is to make usage of the human brain which actually is the most
brilliant data mining system we know.  In connection with our sensory
system we are able to recognize and distinguish patterns, and this
capability is usually exploited when data is presented in form of a
visualization. However, we also have extremely high-developed pattern
recognition capabilites in the auditory domain, and the field of
sonification addresses this modality by rendering auditory representation
for data for the joint purposes of deepening insight into given data and
facilitating the monitoring of complex processes.

An unanswered question is how high-dimensional data could or should sound.
This paper looks at the relation between sound and meaning in our real
world and transfers some findings onto the sonification domain.  The result
is the technique of Model-Based Sonification, which allows the development
of sonifications that can easily be interpreted by the listener.
 

Auditory Perception and Environmental Listening
Data Sonification
Model-Based Sonification
to the paper