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Record W4306754120 · doi:10.1177/03057356221126203

Just noticeable differences in sound intensity of piano tones in non-musicians and experienced pianists

2022· article· en· W4306754120 on OpenAlexaff
Teri Slade, Alex Escolá Gascón, Gilles Comeau, Donald Russell

Bibliographic record

VenuePsychology of Music · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsCarleton UniversityUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsPianoPsychoacousticsPsychologyTone (literature)PerceptionAudiologyIntensity (physics)MusicalMIDIKey (lock)AcousticsCognitive psychologyComputer scienceLinguisticsVisual artsArt

Abstract

fetched live from OpenAlex

Changes in sound intensity are important components of piano performance. Previous research in this area has largely focused on perception of changes in pitch and timing, with little investigation of sound intensity. The present study used Musical Instrument Digital Interface (MIDI) technology to algorithmically control the key velocity, and thereby sound intensity, of consecutive piano tones. Non-musicians ( n = 20) and experienced pianists ( n = 30) played pairs of consecutive piano tones with 0–15 arbitrary units (a.u.) of change in key velocity, indicating whether they perceived the first, second, or neither tone as louder. All participants completed the test with C4, and a subset ( n = 24) with pitches C2 and C6 also. The mean just noticeable difference (JND) ranged from 2.71 to 4.48 a.u., corresponding to 0.68–1.22 dBC on the Yamaha Disklavier used to produce the stimuli. Experienced pianists demonstrated lower JND, which the authors theorize may be linked to listener motivation. When two tones were played with the same key velocity, participants tended to perceive the first tone as louder. The authors discuss a number of factors that may be relevant considerations for application of the findings in music and psychoacoustics research. These findings hold insight for music performance researchers, psychoacoustics researchers, and pedagogues.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.096
GPT teacher head0.334
Teacher spread0.238 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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