Title:
Perceptually-motivated sonification of spatiotemporally-dynamic CFD data
Perceptually-motivated sonification of spatiotemporally-dynamic CFD data
Author(s)
Temor, Lucas
MacDonald, Daniel E.
Natarajan, Thangam
Coppin, Peter W.
Steinman, David A.
MacDonald, Daniel E.
Natarajan, Thangam
Coppin, Peter W.
Steinman, David A.
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Everyday perception and action are fundamentally multisensory. Despite this, the sole reliance on visualization for the representation of complex 3D spatiotemporal data is still widespread. In the past we have proposed various prototypes for the sonification of dense data from computational fluid dynamics (CFD) simulations of turbulent-like blood flow, but did not robustly consider the perception and associated meaning-making of the resultant sounds. To reduce some of the complexities of these data for sonification, in this work we present a feature-based approach, applying ideas from auditory scene analysis to sonify different data features along perceptually-separable auditory streams. As there are many possible features in these dense data, we followed the analogy of "caricature" to guide our definition and subsequent amplification of unique spectral and fluctuating features, while effectively minimizing the features common between simulations. This approach may allow for better insight into the behavior of flow instabilities when compared to our previous sonifications and/or visualizations, and additionally we observed benefits when some redundancy was maintained between modalities.
Sponsor
Date Issued
2021-06
Extent
Resource Type
Text
Resource Subtype
Proceedings
Rights Statement
Licensed under Creative Commons Attribution Non-Commercial 4.0 International License.