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Record W1968562655 · doi:10.1121/1.4785839

Modeling auditory localization in the low-frequency range

2005· article· en· W1968562655 on OpenAlexaff
Jonas Braasch, William L. Martens, Wieslaw Woszczyk

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsBinaural recordingAcousticsOctave (electronics)Sound localizationRange (aeronautics)Noise (video)PsychoacousticsPhysicsLow frequencyMathematicsComputer sciencePerceptionPsychologyMaterials science

Abstract

fetched live from OpenAlex

Binaural recordings were made for subwoofer reproduction of octave-band noise bursts at 31.5-Hz, 63-Hz and 125-Hz center frequencies, and these low-frequency responses were analyzed using a binaural model simulating human perception. As expected, the interaural level differences remained nearly constant for different sound source positions within this low-frequency range. On the basis of interaural time differences, however, the model was able to predict the left/right position of the sound source on the interaural axis. In order to visualize the cross-correlation peak at low frequencies in the ITD range from −1.5 ms to +1.5 ms, the cross-correlation functions were decompressed by taking them to the power of 40. At these low-frequencies, the range of phase difference does not vary much with different sound positions although the ITDs are on the same order as for higher frequencies (≊−1.0 ms to 1.0 ms), but the human ability to resolve very small phase differences already has been shown in previous investigations. The predictions of the model simulation were verified in a listening test. The repetition of the experiment in a second more reverberant space showed similar reductions in performance for both the human listeners and the model. [Work supported by VRQ.]

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 score0.875
Threshold uncertainty score0.309

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 science0.0020.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicSpeech and Audio ProcessingFrench-language works237,207