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Record W2623778022 · doi:10.1121/1.4988663

Piano tone control through variation of “weight” applied on the keys

2017· article· en· W2623778022 on OpenAlexaffabout
Caroline Traube

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPianoAcousticsTone (literature)Variation (astronomy)EscapementComputer scienceSpeech recognitionArtPhysicsLiterature

Abstract

fetched live from OpenAlex

To control the tone of their instrument, piano teachers at University of Montreal recommend to act on the double escapement action by modifying the “weight” applied on the keys. When the pianist uses more weight, the key is pressed to the bottom of the keyboard and the pianists feel a bump when they pass the escapement threshold. When using less “weight”, they play more at the surface of the keyboard. The present study aims to verify if this variation of weight has an impact on the piano tone. Two series of recordings were analysed. In a first series, pianists played a short musical phrase varying several control parameters (with/without weight, with/without pedal) and at several intensity levels. In a second series of recordings, isolated notes were played in the same conditions. Simultaneously to the recording of the piano tones, a video image of double escapement grand piano action was captured with a camera placed inside the piano. The analysis of the data shows that the piano tones produced with and without weight differ along several acoustical descriptors (temporal and spectral features). The main parameters which are modified are related to the quality of the attack.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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 designTheoretical or conceptual
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

Citations4
Published2017
Admission routes2
Has abstractyes

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