Archived Project: Sonification of Sensor Network Data

In particular underwater temperature measurements made via an array of thermometers at Lake Tahoe at various depths.


Mentor: Naoki Saito (Mathematics)

Sonification of sensor network data, more specifically, of underwater temperature measurements made via an array of thermometers at Lake Tahoe installed at various depths. By viewing such temperature measurements at each depth as a time series, we first detrended it. Then, we used the so-called the 'synchrosqueezing' transform to generate a focused time­-frequency plane figure where one can see: 1) major oscillatory events in that time series; and 2) how such events change their oscillation characteristics over time. In that figure, these events are represented as "time-frequency curves". Then, we extracted such time-frequency curves, mapped these curves to pitches in a music scale in the human­ audible range with the trend as volume, and then assigned a synthesized musical instrument to those curves for each time series using the digital audio workstation. We successfully generated a sonified mp3 file, which was presented by Jordan Leung at the 26th Annual Undergraduate Research, Scholarship and Creative Activities Conference held at UC Davis in May. Our sonification of sensor network data, in particular, that of Lake Tahoe temperature measurements, is likely to be useful for non-technical decision makers to appreciate the complexity and the connectivity within such aquatic ecosystems. The reason is that such aquatic ecosystems are extremely rich in its complexity, populated with spatial and temporal phenomena that interact over many orders of magnitude and the information embodied in such complexity is usually only available to a few who are expert in specialized area of mathematics, engineering, and geophysics. Also by presenting this complexity in ways that transcend the underlying mathematics and physics, it is possible to spark curiosity and interest in those who do not currently see themselves destined for careers in the sciences.

MP3 file: Sonification of Sensor Network Data (credits: Alex Berrian, Jordan Leung, and Naoki Saito)

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