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Record W2173129408 · doi:10.1121/1.4934954

Direction-of-arrival estimation of passive acoustic sources in reverberant environments based on the Householder transformation

2015· article· en· W2173129408 on OpenAlexaff
Gongping Huang, Jingdong Chen, Jacob Benesty

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2015
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsShort-time Fourier transformTransformation (genetics)ReverberationMicrophone arrayDirection of arrivalAcousticsComputer scienceNoise (video)MicrophoneFourier transformSIGNAL (programming language)Range (aeronautics)Frequency domainSpeech recognitionAlgorithmMathematicsTelecommunicationsPhysicsMathematical analysisArtificial intelligenceAntenna (radio)EngineeringLoudspeakerFourier analysisComputer vision

Abstract

fetched live from OpenAlex

This paper presents an approach to the direction-of-arrival (DOA) estimation problem in acoustic environments using microphone arrays. It works in the short-time Fourier transform (STFT) domain. It first transforms the noisy speech signals received at the array into the STFT domain. A Householder transformation is then constructed and applied to the multichannel STFT coefficients in each subband. This transformation converts the multichannel STFT coefficients into two components: one is a single coefficient that is dominated by the signal of interest and the other consists of the M - 1 coefficient that is dominated by noise (or even consists of noise-only if there is no reverberation), where M is the number of sensors. A cost function is then formed from the outputs of the Householder transformation and the DOA information can subsequently be obtained by searching the extremum value of this cost function in the angle range between 0° and 180°. Simulation results are provided to illustrate the performance of this approach.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations20
Published2015
Admission routes1
Has abstractyes

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