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Record W2118847155 · doi:10.1109/tnsre.2003.819645

Sonification of range information for 3-D space perception

2003· article· en· W2118847155 on OpenAlexaff
Evangelos Milios, Bill Kapralos, Agnieszka Kopinska, Sotirios Stergiopoulos

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2003
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsYork UniversityDalhousie University
Fundersnot available
KeywordsSonificationSonarComputer scienceRange (aeronautics)SIGNAL (programming language)Classification of discontinuitiesAuditory displayPerceptionAcousticsTask (project management)Space (punctuation)Computer visionArtificial intelligencePhysicsHuman–computer interactionEngineeringMathematicsPsychology

Abstract

fetched live from OpenAlex

We present a device that allows three-dimensional (3-D) space perception by sonification of range information obtained via a point laser range sensor. The laser range sensor is worn by a blindfolded user, who scans space by pointing the laser beam in different directions. The resulting stream of range measurements is then converted to an auditory signal whose frequency or amplitude varies with the range. Our device differs from existing navigation aids for the visually impaired. Such devices use sonar ranging whose primary purpose is to detect obstacles for navigation, a task to which sonar is well suited due to its wide beam width. In contrast, the purpose of our device is to allow users to perceive the details of 3-D space that surrounds them, a task to which sonar is ill suited, due to artifacts generated by multiple reflections and due to its limited range. Preliminary trials demonstrate that the user is able to easily and accurately detect corners and depth discontinuities and to perceive the size of the surrounding space.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.240
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2003
Admission routes1
Has abstractyes

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