Sonification and Animation of Multivariate Data to Illuminate Dynamics of Geyser Eruptions

• Published in: Computer music journal Vol. 44; no. 1; pp. 35 - 50
• Main Authors: Barth, Anna, Karlstrom, Leif, Holtzman, Benjamin K, Paté, Arthur, Nayak, Avinash
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 05.03.2020; The MIT Press; MIT Press Journals, The; Massachusetts Institute of Technology Press (MIT Press)
• Subjects: Animation; Dynamical systems; Education; Engineering Sciences; Geysers; Machine learning; Multivariate analysis; National parks; Representations
• ISSN: 0148-9267; 1531-5169
• DOI: 10.1162/comj_a_00551

Sonification of time series data in natural science has gained increasing attention as an observational and educational tool. Sound is a direct representation for oscillatory data, but for most phenomena, less direct representational methods are necessary. Coupled with animated visual representations of the same data, the visual and auditory systems can work together to identify complex patterns quickly. We developed a multivariate data sonification and visualization approach to explore and convey patterns in a complex dynamic system, Lone Star Geyser in Yellowstone National Park. This geyser has erupted regularly for at least 100 years, with remarkable consistency in the interval between eruptions (three hours) but with significant variations in smaller scale patterns between each eruptive cycle. From a scientific standpoint, the ability to hear structures evolving over time in multiparameter data permits the rapid identification of relationships that might otherwise be overlooked or require significant processing to find. The human auditory system is adept at physical interpretation of call-and-response or causality in polyphonic sounds. Methods developed here for oscillatory and nonstationary data have great potential as scientific observational and educational tools, for data-driven composition with scientific and artistic intent, and towards the development of machine learning tools for pattern identification in complex data.