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Peer Review #2 of "The influence of fundamental frequency on perceived duration in spectrally comparable sounds (v0.1)"

2017· peer-review· en· W4229760569 on OpenAlexaff
Caitlin Dawson, Daniel Aalto, Juraj Šimko, Martti Vainio

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of AlbertaMisericordia Community Hospital
Fundersnot available
KeywordsDuration (music)AudiologyPsychologyAcousticsPhysicsMedicine

Abstract

fetched live from OpenAlex

The perceived duration of a sound is affected by its fundamental frequency and intensity: higher sounds are judged to be longer, as are sounds with greater intensity.Since increasing intensity lengthens the perceived duration of the auditory object, and increasing the fundamental frequency increases the sound's perceived loudness (up to ca.3 kHz), frequency modulation of duration could be potentially explained by a confounding effect where the primary cause of the modulation would be variations in intensity.Here, a series of experiments are described that were designed to disentangle the contributions of fundamental frequency, intensity, and duration to perceived loudness and duration.In two forced-choice tasks, participants judged duration and intensity differences between two sounds varying simultaneously in intensity, fundamental frequency, fundamental frequency gliding range, and duration.The results suggest that fundamental frequency and intensity each have an impact on duration judgments, while frequency gliding range did not influence the present results.We also demonstrate that the modulation of perceived duration by sound fundamental frequency cannot be fully explained by the confounding relationship between frequency and intensity.

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)0.008
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.080
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0040.001
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.2450.151

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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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.

Study designNot applicable
DomainEvaluation
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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