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Record W1604179494

A survey of double-talk detection schemes for echo cancellation applications

2004· article· en· W1604179494 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian acoustics · 2004
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsEcho (communications protocol)Computer scienceEnergy (signal processing)Path (computing)AlgorithmComputational complexity theorySimple (philosophy)Mathematics
DOInot available

Abstract

fetched live from OpenAlex

An echo canceller removes undesired echo in fullduplex speech communication.The cancellation is done by modeling the echo path impulse response with an adaptive finite impulse response filter and subtracting the echo estimate from the received signal.A typical diagram of an echo canceller is depicted in Figure 1.The signal x(n) and v(n) represent the far-end and near-end speeches respectively.The signal s(n) and y(n) represent the echo signal generated by the actual echo path h and the echo estimate produced by the adaptive filter.The signal e(n) denotes the residual error signal, which is transmitted to the far-end side and is used to update the coefficient w of the adaptive filter.x(n) Far-end Adaptive -T Echo Filter Path w h Near-end

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.982

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 communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.260
Teacher spread0.233 · 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