Direct Segmented Sonification of Characteristic Features of the Data Domain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

DOI

Authors

External departments

  • University of Music and Performing Arts, Graz

Details

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Auditory Display (ICAD 2019)
EditorsPaul Vickers, Matti Gröhn, Tony Stockman
Place of PublicationNewcastle upon Tyne
PublisherNorthumbria University
Pages244-253
Number of pages10
ISBN (Electronic)0967090466
DOIs
Publication statusPublished - 27 Jul 2019
EventICAD 2019: The 25th International Conference on Auditory Display - Northumbria University, Newcastle upon Tyne, United Kingdom
Duration: 23 Jun 201927 Jun 2019
https://icad2019.icad.org

Conference

ConferenceICAD 2019: The 25th International Conference on Auditory Display
Abbreviated titleICAD2019
CountryUnited Kingdom
CityNewcastle upon Tyne
Period23/06/1927/06/19
Internet address
Publication type

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Like audification, auditory graphs maintain the temporal relationships of data while using parameter mappings to represent the ordinate values. Such direct approaches have the advantage of presenting the data stream ‘as is’ without the imposed interpretations or accentuation of particular features found in indirect approaches. However, datasets can often be subdivided into short non-overlapping variable length segments that each encapsulate a discrete unit of domain-specific significant information and current direct approaches cannot represent these. We present Direct Segmented Sonification (DSSon) for highlighting the segments’ data distributions as individual sonic events. Using domain knowledge DSSon presents segments as discrete auditory gestalts while retaining the overall temporal regime and relationships of the dataset. The method’s structural decoupling from the sound stream’s formation means playback speed is independent of the individual sonic event durations, thereby offering highly flexible time compression/stretching to allow zooming into or out of the data. DSSon displays high directness, letting the data ‘speak’ for themselves.

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