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Record W3156034365 · doi:10.1121/2.0001399

Study of timbral variation of tenor steelpan mallets through spectral analysis

2019· article· en· W3156034365 on OpenAlexaff
Colin Malloy

Bibliographic record

VenueProceedings of meetings on acoustics · 2019
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMalletTimbreFrench hornComputer scienceFluteAcousticsVariety (cybernetics)EngineeringArtArtificial intelligenceVisual arts

Abstract

fetched live from OpenAlex

The steelpan is an instrument with unique acoustic properties that allow for a wide range of timbral possibilities influenced by the choice of actuator. It is increasingly common for composers and performers to experiment with mallets made from a variety of materials that produce highly differentiated timbres. Examples of mallet types include wood and aluminum shafts covered with rubber tips, chopsticks, dowel rods, and cardboard tubes. Understanding how mallets interact with the steelpan is an important aspect of performance practice. Aside from standard rubber tipped mallets, most other mallets types are homemade and these characterizations will inform mallet design and construction in order to achieve the desired timbral result. While there have been studies analyzing the modes of vibrations of steelpan notes, the interactions between mallets and timbre hasn’t yet been studied and characterized. The goal of this work is to measure and characterize the timbral influence of a wide variety of mallets on a tenor steelpan through low level audio analysis with a focus on spectral features. Audio recordings were made with five different mallets on a tenor steelpan.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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