MétaCan
Menu
Back to cohort
Record W2121561087 · doi:10.1109/icassp.2000.862113

An adaptive subspace approach for speech enhancement

2002· article· en· W2121561087 on OpenAlexaff
Saeed Gazor, A. Rezayee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsQueen's University
Fundersnot available
KeywordsSpeech enhancementSubspace topologySpeech recognitionWhite noiseNoise (video)Computer scienceColors of noiseGaussian noiseEnergy (signal processing)Component (thermodynamics)SilenceAdditive white Gaussian noiseDetectorInterference (communication)Eigenvalues and eigenvectorsBackground noiseGaussianSpeech processingNoise measurementMathematicsAcousticsNoise reductionArtificial intelligenceTelecommunicationsStatisticsPhysics

Abstract

fetched live from OpenAlex

A new algorithm is proposed for enhancement of speech degraded by colored additive interference. It is observed that the noise energy is disparately distributed along each eigenvector. These energies are obtained from noise samples gathered from silence intervals between speech samples. To obtain these silence intervals, we proposed an efficient voice activity detector based on outputs of principle component eigenfilter. Enhancement is performed by modifying each component due to its noise and clean speech energies. Tests indicated that 71% of our listeners preferred the enhanced speech by the above method over former methods for white Gaussian noise, while 80% of them preferred our method when we processed real noisy speech gathered from various environments.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.931
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.260
Teacher spread0.216 · 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 designOther design
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

Citations8
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicSpeech and Audio ProcessingFrench-language works237,207