The field of sonification, which uses auditory presentation of data to replace or augment visualization techniques, is
gaining popularity and acceptance for analysis of “big data” and for assisting analysts who are unable to utilize
traditional visual approaches due to either: 1) visual overload caused by existing displays; 2) concurrent need to perform
critical visually intensive tasks (e.g. operating a vehicle or performing a medical procedure); or 3) visual impairment due
to either temporary environmental factors (e.g. dense smoke) or biological causes.
Sonification tools typically map data values to sound attributes such as pitch, volume, and localization to enable them to
be interpreted via human listening. In more complex problems, the challenge is in creating multi-dimensional
sonifications that are both compelling and listenable, and that have enough discrete features that can be modulated in
ways that allow meaningful discrimination by a listener.
We propose a solution to this problem that incorporates Complex Event Processing (CEP) with speech synthesis. Some
of the more promising sonifications to date use speech synthesis, which is an "instrument" that is amenable to extended
listening, and can also provide a great deal of subtle nuance. These vocal nuances, which can represent a nearly limitless
number of expressive meanings (via a combination of pitch, inflection, volume, and other acoustic factors), are the basis
of our daily communications, and thus have the potential to engage the innate human understanding of these sounds.
Additionally, recent advances in CEP have facilitated the extraction of multi-level hierarchies of information, which is
necessary to bridge the gap between raw data and this type of vocal synthesis. We therefore propose that CEP-enabled
sonifications based on the sound of human utterances could be considered the next logical step in human-centric "big
data" compression and transmission.