Title:
Auditory graphs from denoising real images using fully symmetric convolutional neural networks

dc.contributor.author Cádiz, Rodrigo F.
dc.contributor.author Droppelmann, Lothar
dc.contributor.author Guzmán, Max
dc.contributor.author Tejos, Cristian
dc.contributor.corporatename International Community for Auditory Display
dc.date.accessioned 2022-03-21T16:18:36Z
dc.date.available 2022-03-21T16:18:36Z
dc.date.issued 2021-06
dc.description Presented at the 26th International Conference on Auditory Display (ICAD 2021) 25-28 June 2021, Virtual conference.
dc.description Presented at the 26th International Conference on Auditory Display (ICAD 2021) 25-28 June 2021, Virtual conference.
dc.description.abstract Auditory graphs are a very useful way to deliver numerical information to visually impaired users. Several tools have been proposed for chart data sonification, including audible spreadsheets, custom interfaces, interactive tools and automatic models. In the case of the latter, most of these models are aimed towards the extraction of contextual information and not many solutions have been proposed for the generation of an auditory graph directly from the pixels of an image by the automatic extraction of the underlying data. These kind of tools can dramatically augment the availability and usability of auditory graphs for the visually impaired community. We propose a deep learning-based approach for the generation of an automatic sonification of an image containing a bar or a line chart using only pixel information. In particular, we took a denoising approach to this problem, based on a fully symmetric convolutional neural network architecture. Our results show that this approach works as a basis for the automatic sonification of charts directly from the information contained in the pixels of an image..
dc.identifier.doi https://doi.org/10.21785/icad2021.028
dc.identifier.uri http://hdl.handle.net/1853/66334
dc.publisher Georgia Institute of Technology
dc.publisher Georgia Institute of Technology
dc.publisher.original International Community on Auditory Display
dc.publisher.original International Community for Auditory Display (ICAD)
dc.relation.ispartofseries International Conference on Auditory Display (ICAD)
dc.rights Licensed under Creative Commons Attribution Non-Commercial 4.0 International License.
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.subject Auditory display
dc.title Auditory graphs from denoising real images using fully symmetric convolutional neural networks
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.corporatename Sonification Lab
local.relation.ispartofseries International Conference on Auditory Display (ICAD)
relation.isOrgUnitOfPublication 2727c3e6-abb7-4df0-877f-9f218987b22a
relation.isSeriesOfPublication 6cb90d00-3311-4767-954d-415c9341a358
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