Re(de)fining sonification: project classification strategies in the Data Sonification Archive

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dc.contributor.authorLindborg, PerMagnus
dc.contributor.authorCaiola, Valentina
dc.contributor.authorCiuccarelli, Paolo
dc.contributor.authorChen, Manni
dc.contributor.authorLenzi, Sara
dc.date.accessioned2024-12-04T14:58:29Z
dc.date.available2024-12-04T14:58:29Z
dc.date.issued2024-09
dc.date.updated2024-12-04T14:58:29Z
dc.description.abstractThis 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, the authors analyze six curatorial classification strategies, labelled Goal, Method, User, Macro Topic, Micro Topic, and MediaMix, and discuss their definitions and usefulness for the archive. They then introduce two computational classification strategies, respectively based on clustering of music information retrieval of sonification audio and topic modeling 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. The authors then explored predictive modeling, tentatively achieving results for Goal, Method, and Macro Topic. This points toward 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 toward a mass medium for data representation, communication, and meaning-making.en
dc.description.sponsorshipThe research for this paper by Permangus Lindborg was supported by Strategic Research Grant CityU #11602923 from City University of Hong Kong, Hong Kong SAR, China. Additionally, Valentina Caiola received support from HKPFS, Hong Kong SAR; Manni Chen received support from the University Grants Committee, City University of Hong Kong; Sara Lenzi received support from the Center for Design, Northeastern University; Critical Alarms Lab, Delft University of Technology; and Ikerbasque Foundation for Science, Bilbao, Spain. The DSA infrastructure is hosted at—and maintained by—the Center for Design, Northeastern University.en
dc.identifier.citationLindborg, P., Caiola, V., Ciuccarelli, P., Chen, M., & Lenzi, S. (2024). Re(de)fining sonification: project classification strategies in the Data Sonification Archive. AES: Journal of the Audio Engineering Society, 72(9), 585-602. https://doi.org/10.17743/JAES.2022.0167
dc.identifier.doi10.17743/JAES.2022.0167
dc.identifier.issn1549-4950
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2144
dc.language.isoeng
dc.publisherAudio Engineering Society
dc.titleRe(de)fining sonification: project classification strategies in the Data Sonification Archiveen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.endPage602
oaire.citation.issue9
oaire.citation.startPage585
oaire.citation.titleAES: Journal of the Audio Engineering Society
oaire.citation.volume72
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