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Record W2984885298 · doi:10.1109/imtc.2008.4547171

Nonlinear System Identification Using a Subband Adaptive Volterra Filter

2008· article· en· W2984885298 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsBroadcom (Canada)Carleton University
Fundersnot available
KeywordsLoudspeakerTransfer functionNonlinear systemNonlinear distortionAdaptive filterComputer scienceSystem identificationDistortion (music)Identification (biology)Control theory (sociology)Nonlinear system identificationTelephonyEcho (communications protocol)Filter (signal processing)Volterra seriesLeast mean squares filterSpeech recognitionAlgorithmAcousticsEngineeringTelecommunicationsPhysicsBandwidth (computing)AmplifierData modelingArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a flexible and efficient subband adaptive second order Volterra filter structure for nonlinear system identification. Acoustic echo cancellation is an application of system identification that is critical in hands-free telephony, for which a linear model is usually assumed. However, echo cancellation is limited by inherent system nonlinearities, of which loudspeaker distortion is one of the main sources. Experimental results, obtained in an office room environment, show that the proposed structure was able to model the nonlinear room transfer function more accurately than a linear only model by up to 10 dB lower mean square error, at a comparable computational complexity.

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: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.668

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 head0.044
GPT teacher head0.243
Teacher spread0.198 · 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

Quick stats

Citations10
Published2008
Admission routes1
Has abstractyes

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