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Record W7093662446

Durée perceptuelle correspondante des tons plats et percussifs

2024· other· en· W7093662446 on OpenAlexafffundvenue

Bibliographic record

VenueCanadian acoustics · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDuration (music)AmplitudeTone (literature)Envelope (radar)Active listeningConvergence (economics)Time perceptionConstant (computer programming)
DOInot available

Abstract

fetched live from OpenAlex

The extensive literature on duration assessment generally uses tones with clear onsets and offsets. However, simplistic sounds can fail to evoke the same processes used when listening to sounds with time varying amplitude envelopes (Schutz & Gillard, 2020). Researchers use simplistic sounds to control for extraneous variables between different sounds, with one of these being duration perception. To facilitate future research on the duration assessment of time varying tones, here we explore the ratio at which constant amplitude ‘flat’ and varying amplitude ‘percussive’ tones are perceived as the same duration. We used an adaptive staircase procedure, which presented flat and percussive tones in pairs; participants then stated which tone sounded longer in duration. Each response changed the duration difference on subsequent trials and continued until responses converged around a point, with convergence defined as four consecutive reversals in response direction. One instance of these trials constituted a single staircase; we then calculated the millisecond point of subjective equality (PSE) between flat and percussive tones by finding the average point of convergence between multiple, interleaved staircases starting above and below an initial duration. We found a ratio of flat to percussive duration of approximately 1.67, though with high variance. Consistent, low-variance results from a homogenous (same envelope comparisons) version of the experiment suggest that the adaptive procedure is not the cause. As a result, future iterations will involve collecting large amounts of data from a smaller sample in a controlled lab setting, thereby reducing individual differences and extraneous variables.

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)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.002

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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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 designNot applicable
Domainnot available
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
Published2024
Admission routes3
Has abstractyes

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