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Record W2138035739 · doi:10.1109/tvt.2003.808803

Application of near-field optimum microphone arrays to hands-free mobile telephony

2003· article· en· W2138035739 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

VenueIEEE Transactions on Vehicular Technology · 2003
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsCarleton UniversityNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsBeamformingMicrophone arrayMicrophoneAdaptive beamformerAcousticsNoise (video)IsotropyNoise-canceling microphoneComputer scienceSpeech enhancementPickupElectronic engineeringNoise measurementEngineeringBackground noiseLoudspeakerPhysicsNoise reductionOptics

Abstract

fetched live from OpenAlex

This paper discusses the application of fixed microphone arrays to speech pickup in mobile telephone applications. Array optimization techniques are used to design two broad-band beamformers for speech pickup in the near field. The first beamformer provides optimum gain for spatially incoherent noise while the second beamformer provides optimum gain in spherically isotropic noise. Array performance was measured using vehicular noise recorded under realistic driving conditions. Results obtained are in agreement with theoretical predictions for a spherically isotropic noise field and are comparable to previously reported results obtained using adaptive beamforming algorithms.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.691

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 science0.0010.000
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 head0.005
GPT teacher head0.220
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