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Record W4324145059 · doi:10.3397/in_2022_0467

Subjective studies on floor impact sound using headphone

2023· article· en· W4324145059 on OpenAlexaff
Susumu Hirakawa, Hayato Sato, Manabu Chikai, Atsuo Hiramitsu, Hiroshi Sato, Jeffrey Mahn, Markus Müller-Trapet, Iara Batista da Cunha

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAnechoic chamberBinaural recordingMonauralMicrophoneAmbisonicsAcousticsHeadphonesViolinComputer scienceLoudspeakerPhysics

Abstract

fetched live from OpenAlex

Due to the pandemic situation, it become complex to conduct subjective experiments in the anechoic chamber as a result of the lockdown. For this situation the procedure for the subjective experiments without accessing to the anechoic chamber needs to be considered for an alternative approach. The previous study has shown that there are good correlation between the laboratory and online listening test on impact sounds in residential buildings using ambisonic microphone recording with headphone. Hence, further subjective experiments were carried out with monaural and binaural microphone recordings in an experimental buildings in Japan. The subjective experiment using a headphone in anechoic chamber was held in AIST, Japan. The 3 different floor types, 2 different types of microphones (mono and binaural), at 12 different combinations of impact sources, excitation positions and microphone positions were tested. This study also provided some evidence, and suggested there are potential of the online/remote subjective experiment using headphone.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.355
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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