Title:
Data-to-music API: Real-time data-agnostic sonification with musical structure models

dc.contributor.author Tsuchiya, Takahiko
dc.contributor.author Freeman, Jason
dc.contributor.author Lerner, Lee W.
dc.contributor.corporatename International Community for Auditory Display
dc.contributor.corporatename Georgia Institute of Technology. Center for Music Technology
dc.contributor.corporatename Georgia Tech Research Institute. Configurable Computing & Embedded Systems Laboratory
dc.date.accessioned 2015-11-06T15:01:46Z
dc.date.available 2015-11-06T15:01:46Z
dc.date.issued 2015-07
dc.description Presented at the 21st International Conference on Auditory Display (ICAD2015), July 6-10, 2015, Graz, Styria, Austria. en_US
dc.description Presented at the 21st International Conference on Auditory Display (ICAD2015), July 6-10, 2015, Graz, Styria, Austria.
dc.description.abstract In sonification methodologies that aim to represent the underlying data accurately, musical or artistic approaches are often dismissed as being not transparent, likely to distort the data, not generalizable, or not reusable for different data types. Scientific applications for sonification have been, therefore, hesitant to use approaches guided by artistic aesthetics and musical expressivity. All sonifications, however, may have musical effects on listeners, as our trained ears with daily exposure to music tend to naturally distinguish musical and non-musical sound relationships, such as harmony, rhythmic stability, or timbral balance. This study proposes to take advantage of the musical effects of sonification in a systematic manner. Data may be mapped to high-level musical parameters rather than to one-to-one low-level audio parameters. An approach to create models that encapsulate modulatable musical structures is proposed in the context of the new DataTo- Music JavaScript API. The API provides an environment for rapid development of data-agnostic sonification applications in a web browser, with a model-based modular musical structure system. The proposed model system is compared to existing sonification frameworks as well as music theory and composition models. Also, issues regarding the distortion of original data, transparency, and reusability of musical models are discussed. en_US
dc.embargo.terms null en_US
dc.identifier.citation Tsuchiya, T., et al. "Data-to-music API: Real-time data-agnostic sonification with musical structure models". In K. Vogt, A. Andreopoulou, & V. Goudarzi, eds. Proceedings of the 21st International Conference on Auditory Display (ICAD 2015). July 6-10, 2015, Graz, Styria, Austria. en_US
dc.identifier.isbn 978-3-902949-01-1
dc.identifier.uri http://hdl.handle.net/1853/54146
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher Georgia Institute of Technology
dc.publisher.original University of Music and Performing Arts Graz. Institute of Electronic Music and Acoustics
dc.publisher.original International Community for Auditory Display (ICAD)
dc.relation.ispartofseries International Conference on Auditory Display (ICAD)
dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.subject Auditory display en_US
dc.subject Sonification methodologies en_US
dc.subject Musical structure systems en_US
dc.title Data-to-music API: Real-time data-agnostic sonification with musical structure models en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Freeman, Jason
local.contributor.corporatename Sonification Lab
local.relation.ispartofseries International Conference on Auditory Display (ICAD)
relation.isAuthorOfPublication 9dbee332-a96b-4661-a5d5-3bbd54cf71b8
relation.isOrgUnitOfPublication 2727c3e6-abb7-4df0-877f-9f218987b22a
relation.isSeriesOfPublication 6cb90d00-3311-4767-954d-415c9341a358
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