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Record W4249003267 · doi:10.1121/1.4799670

Sensorial substitution system from vision to audition using transparent digital earplugs

2013· article· en· W4249003267 on OpenAlexafffund
Damien Lescal, Jean Rouat, Jérémie Voix

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie SupérieureUniversité de Sherbrooke
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité de Sherbrooke
KeywordsSensory substitutionModality (human–computer interaction)Computer scienceSubstitution (logic)Computer visionArtificial intelligenceHuman–computer interactionSensory systemPsychology

Abstract

fetched live from OpenAlex

Since the TVSS (Tactile Vision Substitution System) developed by Bach-Y-Rita in 1960's, several sensorial substitution systems have been developed. In general, the so-called "sensorial substitution" system transform stimuli characteristic of one sensory modality (for example, vision) into stimuli of another sensory modality (for example, audition). These systems are developed to help handicapped persons. We developed a sensorial substitution system from vision to audition. An artificial neural network is used to identify the important parts in the image. The Virtual Acoustic Space technic is used to generate localizable sounds. A sound is associated to each important parts of the image. The entire real-time system has been implemented on iOS platforms (iPhone/iPad/iPod Touch{trade mark, serif}). We associated our system with transparent digital earplugs. This way the user is aware of the audio scene happening around him. The system has been tested on non-blind persons and the results are presented.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.278
Teacher spread0.240 · 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 designBench or experimental
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

Citations4
Published2013
Admission routes2
Has abstractyes

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