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Record W4389273264 · doi:10.3397/in_2023_0300

Evaluation of the Respective Effects of Microphone Type and Dummy Head Type on Measured Head-Related Transfer Functions

2023· article· en· W4389273264 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

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversité du Québec à MontréalCentre for Interdisciplinary Research in Music Media and TechnologyMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsHead (geology)Binaural recordingMicrophoneHeadphonesAcousticsHead-related transfer functionTransfer functionAnechoic chamberComputer scienceAzimuthLoudspeakerMathematicsPhysicsGeologyEngineeringGeometry

Abstract

fetched live from OpenAlex

A dummy head is a tool used to generate binaural recordings, i.e. recordings when listened to through headphones that allow for the listener to hear from the dummy's perspective. A dummy head typically includes pinnae and ear canals in which microphones are placed. The global shape of a dummy head replicates an average-sized human head, and variations around this average will depend on the manufacturer (pinnae of different size, presence of a nose or a mouth). This paper describes measurements conducted on two dummy heads in order to evaluate and rank the respective influence of microphone type and dummy head shape on spatialization cues. Four possible cross configurations are tested, that is head 1 or 2 with microphones pair 1 or 2. For each configuration, head-related transfer functions are measured in an anechoic room for various azimuth and elevation angles. The results obtained show that the effect of the dummy head shape is larger than the effect of the microphone type on measured head-related transfer functions. A practical implication of this result is found when microphones have to be replaced on a given dummy head.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.036
GPT teacher head0.280
Teacher spread0.244 · 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