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Record W2098214100 · doi:10.1109/icassp.2012.6287826

Single-channel noise reduction in the STFT domain based on the bifrequency spectrum

2012· article· en· W2098214100 on OpenAlexaff
Jingdong Chen, Jacob Benesty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsShort-time Fourier transformFast Fourier transformFourier transformFrequency domainNoise (video)Reduction (mathematics)Spectrum (functional analysis)AlgorithmComputer scienceMathematicsSpeech recognitionArtificial intelligencePhysicsFourier analysisMathematical analysis

Abstract

fetched live from OpenAlex

This paper studies the problem of noise reduction in the short-time Fourier transform (STFT) domain. Traditionally, the STFT coefficients in different frequency bands are assumed to be independent. This assumption holds when the signals are stationary and the fast Fourier transform(FFT) length is sufficiently large. In practice, however, speech is nonstationary and also the FFT length cannot be very large due to practical reasons. So, there always exists some correlation between STFT coefficients from neighboring frequency bands. An important question then arises: how the interband correlation can be used to optimize noise reduction performance? This paper addresses this issue. We discuss two solutions in the framework of the bifrequency spectrum. One considers the cross-correlation between all the frequency bands and the other takes into account only the cross-correlation between neighboring bands. While the former is optimal from a theoretical perspective, the latter is more practical as it is more immune to the error in correlation matrix estimation.

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.

How this classification was reachedexpand

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)0.001
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 score0.650
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.231
Teacher spread0.202 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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