Re(de)fining Sonification: Project Classification Strategies in the Data Sonification Archive

This study focuses on a corpus of 445 sonification projects currently available in the Data Sonification Archive (DSA). The DSA develops in a collaborative process that involves researchers and creative communities, and has been online since early 2021. Projects are heuristically classified according to several aspects, in particular their intended purpose, targeted users, subject matter, sonification method, and combination of media. In the present study, we analyse six curatorial classification strategies, labelled <i>Goal</i>, <i>Method</i>, <i>User,</i> <i>Macro Topic</i>, <i>Micro Topic</i>, and <i>MediaMix</i>, and discuss their definitions and usefulness for the archive. We then introduce two computational classification strategies, respectively based on clustering of music information retrieval of sonification audio, and topic modelling of the descriptive texts that accompany DSA projects. Correlation analysis between curatorial and computational classifications, correspondingly sized, showed that the text-based method was more powerful than the audio-based methods. We then explored predictive modelling, tentatively achieving results for <i>Goal, Method, and Macro Topic</i>. This points towards the potential for automatic classification to assist in the curatorial management of the archive, as well as for similar repositories. The discussion focuses on how analysis of classification strategies supports a broadening of the definition of sonification, both as theoretical construct and as practice, where the communicative intention of the author, the aesthetic quality of the listening experience, a more explicit focus on narrative patterns, and other emerging aspects within sonification design, are all contributing factors to transitioning the field towards a mass medium for data representation, communication, and meaning-making.