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Idan Zelzer

dblp:179/4828 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 50% Personal fabrication and tangible interfaces · 50%
Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › audio classification
sound classification
0.112016
Tickers and Talker: An Accessible Labeling Toolkit for 3D Printed Models · CHI 2016

Methods — techniques the papers use, named apart from their topics

signal processing · 0.5audio classification · 0.5
YearPublicationVenuePosition
2016 Tickers and Talker: An Accessible Labeling Toolkit for 3D Printed Models
abstract
Three-dimensional models are important learning resources for blind people. With advances in 3D printing, 3D models are becoming more available. However, unlike visual or tactile graphics, there is no standard accessible way to label components in 3D models. We present a labeling toolkit that enables users to add and access audio labels to 3D printed models. The toolkit includes Tickers, small 3D printed percussion instruments added to 3D models, and Talker, a signal processing application that detects and classifies Ticker sounds. To use the toolkit, a model designer adds Tickers to a model using 3D modeling software. A user then prints the model with Tickers and records audio labels for each Ticker. Finally, users can strum the Tickers and Talker will play the corresponding labels. We evaluated Tickers and Talker with three models in a study with nine blind participants. Our toolkit achieved an accuracy of 93% across all participants and models. We discuss design implications and future work for accessible 3D printed models.
Lei Shi 0020, Idan Zelzer, Catherine Feng, Shiri Azenkot
CHI2