Researchers in the field of human-technology interaction have demonstrated how a custom-built 'data-to-music' algorithms can help to better understand complex data. The transformation of digital data into sounds could be a game-changer in the growing world of data interpretation.
A team of researchers in the field of human-technology interaction at Tampere University and Eastern Washington University has demonstrated how a custom-built"data-to-music" algorithms can help us to better understand complex data. The transformation of digital data into sounds could be a game-changer in the growing world of data interpretation.
The lead author of the article is Jonathan Middleton, DMA, a professor of music theory and composition at Eastern Washington University, and a visiting researcher at Tampere University. Middleton and his co-investigators were primarily concerned with showing how a custom-built"data-to-music" algorithms could enhance engagement with complex data points. In their research they used data collected from Finnish weather records.
"Musical sounds can be a highly engaging art form in terms of pure listening entertainment and, as such, a powerful complement to theater, film, video games, sports, and ballet. Since musical sounds can be highly engaging, this research offers new opportunities to understand and interpret data as well as through our aural senses," Middleton explains.
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