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Salient sights and sounds: comparing visual and auditory stimuli remembrance using audio set ontology and sonic mapping
KTH.
KTH.
KTH.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0003-4259-484X
2023 (English)In: SMC 2023: Proceedings of the Sound and Music Computing Conference 2023, Sound and Music Computing Network , 2023, p. 426-432Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we explore how store customers recall their perceptual experience with a focus on comparing the remembrance of auditory and visual stimuli. The study was carried out using a novel mixed-methods approach that involved Deep Hanging Out, field study and interviews, including drawing sonic mind maps. The data collected was analysed using thematic analysis, sound classification with the Audio Set ontology, counting occurrences of different auditory and visual elements attended in the store, and rating the richness of their descriptions. The results showed that sights were more salient than sounds and that participants recalled music more frequently compared to the Deep Hanging Out observations, but remembered fewer varieties of sounds in general.

Place, publisher, year, edition, pages
Sound and Music Computing Network , 2023. p. 426-432
National Category
Musicology
Identifiers
URN: urn:nbn:se:kth:diva-337825Scopus ID: 2-s2.0-85171737933OAI: oai:DiVA.org:kth-337825DiVA, id: diva2:1803504
Conference
20th Sound and Music Computing Conference, SMC 2023, Hybrid, Stockholm, Sweden, Jun 15 2023 - Jun 17 2023
Note

Part of ISBN 9789152773727

QC 20231123

Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-11-23Bibliographically approved

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McHugh, LauraWu, Chih WeiXu, XuanlingFalkenberg, Kjetil

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf