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

Lindborg P, Caiola V, Ciuccarelli P, Chen M, Lenzi S. Re(de)fining Sonification: Project Classification Strategies in the Data Sonification Archive. J Audio Eng Soc. 2024;72(9):585-602. doi:10.17743/jaes.2022.0167

@article{Lindborg2024_22710,
  author = {Lindborg, PerMagnus and Caiola, Valentina and Ciuccarelli, Paolo and Chen, Manni and Lenzi, Sara},
  title = {{Re(de)fining Sonification: Project Classification Strategies in the Data Sonification Archive}},
  journal = {Journal of the Audio Engineering Society},
  volume = {72},
  number = {9},
  pages = {585--602},
  year = {2024},
  month = sep,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0167},
  url = {https://doi.org/10.17743/jaes.2022.0167}
}

TY  - JOUR
TI  - Re(de)fining Sonification: Project Classification Strategies in the Data Sonification Archive
AU  - Lindborg, PerMagnus
AU  - Caiola, Valentina
AU  - Ciuccarelli, Paolo
AU  - Chen, Manni
AU  - Lenzi, Sara
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 72
IS  - 9
SP  - 585
EP  - 602
PY  - 2024
DA  - 2024/09/16
DO  - 10.17743/jaes.2022.0167
UR  - https://doi.org/10.17743/jaes.2022.0167
PB  - Audio Engineering Society
LA  - en
AB  - 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, 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.
ER  -