Abstract: The results of machine learning models can often be difficult to interpret, especially for domain experts. Audio Explorer, the winning entry of the 2018 VAST Challenge, is an interactive data exploration tool that effectively communicates machine learning results, using coordinated geospatial, temporal, and auditory visualizations to promote information discovery.
Note: Kris Cook and I contributed the sidebar for this article. The main article describes work by the first three authors.
Recommended citation: Colin Scruggs, Cameron Henkel, Charles Stolper, Kristen Cook, and R. Jordan Crouser. (2019) Blending machine learning and interaction design in Audio Explorer. IEEE Computer Graphics and Applications.