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Record W2312756559 · doi:10.3813/aaa.918464

Perceptual Evaluation of Rolling Sound Synthesis

2011· article· en· W2312756559 on OpenAlexfundno aff
Emma Murphy, Mathieu Lagrange, Gary Scavone, Philippe Depalle, Catherine Guastavino

Bibliographic record

VenueActa acustica united with Acustica · 2011
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActive listeningPerceptionSpeech recognitionComputer scienceSound (geography)Sound analysisAcousticsPsychologyCommunication

Abstract

fetched live from OpenAlex

Three listening tests were conducted to perceptually evaluate different versions of a new real-time synthesis approach for sounds of sustained contact interactions. This study aims to identify the most effective algorithm to create a realistic sound for rolling objects. In Experiment 1 and 2, participants were asked to rate the extent to which 6 different versions sounded like rolling sounds. Subsequently, in Experiment 3, participants compared the 6 versions best rated in Experiment 1 and 2, to the original recordings. Results are presented in terms of both statistical analysis of the most effective synthesis algorithm and qualitative user comments. On methodological grounds, the comparison of Experiments 1, 2 and 3 highlights major differences between judgments collected in reference to the original recordings as opposed to judgments based on memory representations of rolling sounds.

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)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.076
GPT teacher head0.260
Teacher spread0.185 · 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 designBench or experimental
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

Citations3
Published2011
Admission routes1
Has abstractyes

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