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Record W2129309805 · doi:10.1109/icosp.2006.344536

A Novel Hemispherical Array Sound Source Localization

2006· article· en· W2129309805 on OpenAlexaff
Hedayat Alghassi, Shahram Tafazoli, Peter Lawrence

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrophoneMicrophone arrayAcousticsAcoustic source localizationComputer scienceSIGNAL (programming language)ClosenessSimilarity (geometry)Signal processingAlgorithmSound (geography)PhysicsMathematicsComputer visionDigital signal processingSound pressureMathematical analysis

Abstract

fetched live from OpenAlex

In this paper we present a novel signal processing algorithm and array for sound source localization in an enclosed area. This method, which has some similarity to the human eye structure, consists of a novel hemispherical microphone array with microphones on the shell and one microphone in the sphere center. A signal processing scheme utilizes parallel creation of a special closeness functions for each microphone direction on the shell. The closeness functions have output values that are linearly proportional to spatial angular difference between the sound source direction and each of the shell microphone directions, in close vicinity of them. Finally by choosing directions corresponding to the highest closeness function values and implementing a linear weighted spatial averaging on those directions we estimate the sound source direction. Contrary to traditional algorithmic sound source localization techniques, our method is based on some simple parallel mathematical calculations in the time domain; therefore it can be easily implemented on a custom designed integrated circuit

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)0.000
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score0.238

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 head0.010
GPT teacher head0.218
Teacher spread0.209 · 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
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

Citations3
Published2006
Admission routes1
Has abstractyes

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