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Record W1561493983

Using Computer Vision Technology to Play Gramophone Records

2011· article· en· W1561493983 on OpenAlex
Baozhong Tian, John A. Barron

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Audio Engineering Society · 2011
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsWestern University
Fundersnot available
KeywordsOrientation (vector space)Groove (engineering)Computer visionSIGNAL (programming language)Computer scienceArtificial intelligenceSound (geography)AcousticsComputer graphics (images)OpticsMaterials sciencePhysicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

We present a non-contact optical flow based method to reproduce the sound signal from gramophone records using 3D robust scene reconstruction of the surface orientation of the walls of the grooves. The conversion of analogue data to digital data is an important task for the preservation of historic sound recordings. We digitally viewed the grooves of a record using a microscope that was modified to overcome the limitation of a shallow depth of field by using a thin glass plate to obtain part of the image at a second focal length to gain better overall quality images of the groove. The sound signal was recovered from the groove surface orientation. The overall algorithm has been tested and found to be working correctly using undamaged and damaged real records.

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.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.658
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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