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Record W2176947654 · doi:10.1121/1.4933472

On robust time delay estimation in room acoustic environments

2015· article· en· W2176947654 on OpenAlexaff
Jingdong Chen, Jacob Benesty, Gongping Huang

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 Scientifique
Fundersnot available
KeywordsReverberationRobustness (evolution)Computer scienceRedundancy (engineering)AcousticsSpeech recognition

Abstract

fetched live from OpenAlex

Time delay estimation, which serves as the first stage that feeds into subsequent processors for localizing and tracking radiating sources, has been an active research topic for decades. This paper deals with time delay estimation in room acoustic environments. The major focus is on developing algorithms that can achieve robust time delay estimates in reverberant and noisy environments. Three approaches are investigated, i.e., the blind channel identification based method, the multichannel cross correlation based technique, and a method based on the use of the Householder transformation. The first approach achieves its robustness with respect to reverberation by taking into account reverberation in the problem formulation; the second one improves the robustness by exploiting the redundancy provided by multiple microphones, and the third method attempts to separate noise and reverberation from the signal components. Experimental results are presented to illustrate their performance and pros and cons in room acoustic environments where reverberation and noise are commonly encountered.

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: Methods · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score0.224

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

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