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Record W1976074135 · doi:10.1121/1.3588188

Accurate sound localization via head movements in listeners with precipitous high-frequency hearing loss.

2011· article· en· W1976074135 on OpenAlexaff
Ewan A. Macpherson, M. Alasdair Cumming, Robert W. Quelch

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsWestern University
Fundersnot available
KeywordsAcousticsSound localizationWidebandRotation (mathematics)PhysicsHorizontal planeNoise (video)Stimulus (psychology)AudiologyComputer scienceMathematicsOpticsPsychologyMedicine

Abstract

fetched live from OpenAlex

Information about sound source location in the vertical plane is available via the direction-dependent filtering performed by the outer ears, but errors of localization such as front/rear reversals can occur when stimuli contain a limited range of frequencies or when high frequencies are inaudible due to hearing impairment. Information about front/rear sound source location is also available in the relationship between the rotation of the head and the resulting changes in interaural time and level differences. We have shown previously [Macpherson, J. Acoust. Soc. Am. 125, 2691(A) (2009)] that in normally hearing listeners, a minimum head movement angle (MHMA) of 5–10 deg is sufficient for accurate front/rear localization of low-frequency (0.5–1 kHz) noise-band targets. In the present study, we measured MHMAs for low-frequency and wideband (0.5–16 kHz) targets in listeners with near-normal low-frequency thresholds but precipitous hearing loss above 1–2 kHz. Neither stimulus could be localized accurately by these listeners without head movement, but for both stimuli, MHMAs of 5–10 deg sufficed for accurate localization at a rotation velocity of 50 deg/s. MHMAs increased with increasing rotation velocity similarly to those of normally hearing listeners. The results suggest that listeners with normal hearing and with high-frequency loss benefit similarly from dynamic localization cues. [Work supported by the NSF.]

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

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