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Record W2167894290

Subspace-based speech enhancement by updating noise characteristics in the presence of speech

2008· article· en· W2167894290 on OpenAlex

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

VenueEuropean Signal Processing Conference · 2008
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversité de MonctonInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSubspace topologySignal subspaceSpeech enhancementSpeech recognitionComputer scienceNoise (video)Principal component analysisDistortion (music)Variance (accounting)Pattern recognition (psychology)Noise reductionNoise measurementSIGNAL (programming language)Selection (genetic algorithm)Signal-to-noise ratio (imaging)Artificial intelligenceAlgorithmTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

We present in this paper a signal subspace-based approach for enhancing a noisy signal. In our previous works we have developed an algorithm based on principal component analysis (PCA) in which the optimal subspace selection is provided by a variance of the reconstruction error (VRE) criterion. In this work we will improve our previous technique by applying an updating noise variance algorithm. The performance evaluations show that our method provides a higher noise reduction and a lower signal distortion than our previous one.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.031
GPT teacher head0.245
Teacher spread0.215 · 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