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Record W1992738049 · doi:10.1121/1.2404625

The binaural performance of a cross-talk cancellation system with matched or mismatched setup and playback acoustics

2007· letter· en· W1992738049 on OpenAlexfundno aff
Michael A. Akeroyd, John Chambers, D Bullock, Alan R. Palmer, A. Quentin Summerfield, P.A. Nelson, Stuart Gatehouse

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typeletter
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
FundersMedical Research CouncilUniversity of NottinghamMcGill University
KeywordsBinaural recordingLoudspeakerAcousticsAzimuthComputer scienceTransfer functionWidebandBandwidth (computing)PhysicsTelecommunicationsOpticsEngineering

Abstract

fetched live from OpenAlex

Cross-talk cancellation is a method for synthesizing virtual auditory space using loudspeakers. One implementation is the "Optimal Source Distribution" technique [T. Takeuchi and P. Nelson, J. Acoust. Soc. Am. 112, 2786-2797 (2002)], in which the audio bandwidth is split across three pairs of loudspeakers, placed at azimuths of +/-90 degrees, +/-15 degrees, and +/-3 degrees, conveying low, mid, and high frequencies, respectively. A computational simulation of this system was developed and verified against measurements made on an acoustic system using a manikin. Both the acoustic system and the simulation gave a wideband average cancellation of almost 25 dB. The simulation showed that when there was a mismatch between the head-related transfer functions used to set up the system and those of the final listener, the cancellation was reduced to an average of 13 dB. Moreover, in this case the binaural interaural time differences and interaural level differences delivered by the simulation of the optimal source distribution (OSD) system often differed from the target values. It is concluded that only when the OSD system is set up with "matched" head-related transfer functions can it deliver accurate binaural cues.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.274
Teacher spread0.254 · 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.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations43
Published2007
Admission routes1
Has abstractyes

Explore more

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