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Record W4400288224 · doi:10.1121/10.0026636

Evaluating the perceived annoyance from impact sounds: Validation of previous experiments with an expanded set of recordings

2024· article· en· W4400288224 on OpenAlexaffabout
Sabrina Skoda, Jeffrey Mahn, Iara Batista da Cunha

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAnnoyanceSet (abstract data type)PsychologyAudiologyCognitive psychologySpeech recognitionApplied psychologyComputer scienceLoudnessMedicine

Abstract

fetched live from OpenAlex

Impact noise from neighbors in multi-unit residential buildings is commonly seen as an annoyance that may reduce the quality of life of building occupants. A project at the National Research Council of Canada has been evaluating the perceived annoyance from different types of impact sound, through the implementation of listening experiments in Canada, Korea, and Germany. A comparison among the countries revealed that test participants agreed on the relative annoyance of different impact sources, but the absolute levels of the annoyance were different between participants from the three countries. To explain the differences between the participants from the three countries, moderating factors, such as the test participants’ housing situation and noise sensitivity, were taken into account. In order to validate the previous findings, the results from a further listening experiment conducted at the National Research Council using the same methodology but with a new set of impact sounds recorded on a wider variety of floor-ceiling assembly types will be presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.209

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 science0.0000.000
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 head0.335
Teacher spread0.302 · 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
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicVehicle Noise and Vibration ControlFrench-language works237,207