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Record W103781663

Sinusoidal Parameter Extraction and Component Selection in a Non Stationary Model

2002· preprint· fr· W103781663 on OpenAlexaff
Mathieu Lagrange, Sylvain Marchand, Jean-Bernard Rault

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2002
Typepreprint
Languagefr
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsComputer scienceComponent (thermodynamics)Fourier transformSelection (genetic algorithm)Frequency modulationProcess (computing)AmplitudeEstimation theoryExtraction (chemistry)AlgorithmFourier seriesTracking (education)Spectral density estimationArtificial intelligenceMathematicsPhysicsTelecommunicationsRadio frequencyOptics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we introduce a new analysis technique particu-larly suitable for the sinusoidal modeling of non-stationary signals. This method, based on amplitude and frequency modulation esti-mation, aims at improving traditional Fourier parameters and en-ables us to introduce a new peak selection process, so that only peaks having coherent parameters are considered in subsequent stages (e.g. partial tracking, synthesis). This allows our spectral model to better handle natural sounds.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.244
Teacher spread0.221 · 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.

Study designSimulation or modeling
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

Citations21
Published2002
Admission routes1
Has abstractyes

Explore more

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