MétaCan
Menu
Back to cohort
Record W2106361725 · doi:10.1109/mwscas.2007.4488607

Continuous wavelet transform based source separation

2007· article· en· W2106361725 on OpenAlexaff
Lee-Pierre Belley, M. Gabrea, Christian Gargour

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsShort-time Fourier transformWavelet transformContinuous wavelet transformHarmonic wavelet transformConstant Q transformComputer scienceFourier transformWaveletArtificial intelligenceBlind signal separationIndependent component analysisPattern recognition (psychology)Time–frequency analysisSecond-generation wavelet transformDiscrete wavelet transformSource separationStationary wavelet transformS transformSpeech recognitionMathematicsFourier analysisComputer visionTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Separation of convolutive mixtures of speech sources is considered in this paper. Several approaches have been reported in the literature using statistical methods as well as transforms such as the short time Fourier transform (STFT) and the Paquet wavelet transform (PWT). In this paper we propose a new source separation method based on the independent component analysis (ICA) and utilizing the continuous wavelet transform (CWT). The experimental results obtained by our method have been investigated and compared with those generated by other approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueConference proceedingsSame topicBlind Source Separation TechniquesFrench-language works237,207