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Record W2151935001 · doi:10.1109/icassp.2004.1326337

Comparative study of three time-frequency representations with applications to a novel correlation method

2004· article· en· W2151935001 on OpenAlexaff
Ervin Sejdić, Jin Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsTime–frequency analysisFrequency domainComputer scienceSIGNAL (programming language)CorrelationAlgorithmTime domainSignal processingRepresentation (politics)Time–frequency representationSpeech recognitionPattern recognition (psychology)Artificial intelligenceMathematicsComputer visionTelecommunications

Abstract

fetched live from OpenAlex

The effect of three time-frequency representations on a novel correlation algorithm is studied. By representing a signal in the time-frequency domain, a redundant representation of the signal is obtained. The algorithm presented relies on such redundancies to extrapolate some significant features of the signal. The developed scheme has been applied to heart sound analysis using real recordings from patients, where the opening snap (OS) is distinguished from the third heart sound (S3). The results for the three time-frequency transforms are compared and very encouraging results have been obtained with S-transform.

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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.281

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 science0.0000.000
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 head0.050
GPT teacher head0.381
Teacher spread0.331 · 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 designObservational
Domainnot available
GenreMethods

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

Citations25
Published2004
Admission routes1
Has abstractyes

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