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Record W2052628454 · doi:10.1109/97.855449

GMDF for noise reduction and echo cancellation

2000· article· en· W2052628454 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 Signal Processing Letters · 2000
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsCarleton University
Fundersnot available
KeywordsEcho (communications protocol)Noise reductionReduction (mathematics)MicrophoneNoise (video)Frequency domainTelephonyAlgorithmSpeech enhancementComputer scienceComputationBlock (permutation group theory)Signal-to-noise ratio (imaging)Return lossSpeech recognitionTelecommunicationsMathematicsArtificial intelligenceAntenna (radio)Computer network

Abstract

fetched live from OpenAlex

A noise-reduction (NR) enhancement to the generalized multi-delay frequency domain algorithm (GMDF) involving minimal additional computations is presented. Findings are presented on the ability of the algorithm to suppress street, office, air-conditioning and car noise from the microphone signal in hands-free telephony. For an additional eleven N/P real multiplies and eight N/P real additions per output sample, where N is the block size and P is the amount of output samples per GMDF iteration, results show an average increase in echo return loss enhancement (ERLE) of 9 dB at low SNR (-5 dB) and 6 dB at higher SNR (15 dB).

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 score0.680
Threshold uncertainty score0.529

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 science0.0000.000
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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