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Record W2154245495 · doi:10.1109/tim.2006.884138

Nonintrusive Measurement of Echo-Path Parameters in VoIP Environments

2006· article· en· W2154245495 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 Instrumentation and Measurement · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsNortel (Canada)Carleton University
Fundersnot available
KeywordsEcho (communications protocol)Computer scienceCodecDistortion (music)Voice over IPNonlinear distortionPath (computing)Packet lossComputationNetwork packetReduction (mathematics)Path lossReal-time computingElectronic engineeringAlgorithmThe InternetComputer networkTelecommunicationsEngineeringBandwidth (computing)WirelessMathematics

Abstract

fetched live from OpenAlex

This paper proposes two echo-path delay measurement methods suitable for voice-over-Internet-protocol environments, where the echo suffers from excessive delay and nonlinear distortion. The proposed methods aim at greatly reducing the computational requirements while maintaining good measurement accuracy. The delay measurement is based on the cross correlation; the computation reduction is achieved by using either downsampled speeches or sparse speeches for the two methods, respectively. The echo-path loss is also measured by using the obtained delay information. The performance under codec distortion, packet loss, noise, and double talk conditions is examined through simulations and real field measurements. The results show that the proposed methods are effective and accurate

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: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.761

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 head0.023
GPT teacher head0.215
Teacher spread0.192 · 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