MétaCan
Menu
Back to cohort
Record W1503075868

Experimental evaluation of vehicle cabin noise using subjective and objective psychoacoustic analysis techniques

2011· article· en· W1503075868 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of Windsor
FundersFord Motor Company
KeywordsPsychoacousticsNoise, vibration, and harshnessAcousticsVibrationNoise (video)HarshnessSound qualitySuspension (topology)Sound pressureEngineeringAutomotive industryAutomotive engineeringComputer sciencePerceptionMathematicsPhysicsAerospace engineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Given the automotive industry's awareness of the importance of the perception of noise, vibration and harshness (NVH) emissions, there is an increased focus on the sound quality of automotive vehicle cabin noise.Psychoacoustic analysis using acoustic pressure measurements taken inside the vehicle cabin was performed.Suspension vibration measurements from several structural positions were also taken to evaluate vibration excitations.The goal was to be able to predict the psychoacoustic impact at the driver's ear position using the suspension vibration data measured outside the vehicle.Using the vibration data, it was possible to evaluate the transfer path of the excitation energy into the vehicle cabin.Using this, a correlation between the predicted in-cabin psychoacoustic results using the outside vibration measurement data and the direct psychoacoustic calculations from the in-cabin noise measurements was proven possible with some inherent limitations.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.995

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 head0.033
GPT teacher head0.263
Teacher spread0.229 · 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