Self Organizing Maps for the Visual Analysis of Pitch Contours

Abstract

We present a novel interactive approach for the visual analysis of intonation contours. Audio data are processed algorithmically and presented to researchers through interactive visualizations. To this end, we automatically analyze the data using machine learning in order to find groups or patterns. These results are visualized with respect to meta-data. We present a flexible, interactive system for the analysis of prosodic data. Using real-world application examples, one containing preprocessed, the other raw data, we demonstrate that our system enables researchers to interact dynamically with the data at several levels and by means of different types of visualizations, thus arriving at a better understanding of the data via a cycle of hypothesis generation and testing that takes full advantage of our visual processing abilities.