MétaCan
Menu
Back to cohort
Record W4320084302 · doi:10.1121/2.0001678

Acoustical Analysis of the Chinese Transverse Flute (dizi) using the Transfer Matrix Method

2022· article· en· W4320084302 on OpenAlexaff
Xinmeng Luan, Song Wang, Zijin Li, Gary Scavone

Bibliographic record

VenueProceedings of meetings on acoustics · 2022
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsAcousticsTransfer-matrix method (optics)Transfer matrixTransfer functionFluteMatrix (chemical analysis)Materials scienceTransverse planePhysicsGeologyComputer scienceStructural engineeringEngineeringOpticsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

The dizi is a flute-like traditional Chinese wind instrument with a cylindrical bore and a series of holes opening along its length. In addition to the common embouchure hole and six finger holes, there is a membrane hole located between the embouchure hole and the uppermost finger hole, and four extra toneholes placed near the bottom of the bore, which are always open to the air. In this paper, the transfer matrix method (TMM), as well as the transfer matrix method with external interactions (TMMI), are used to study the acoustic characteristics of the dizi. The TMM and TMMI models are validated by comparing the simulated input impedance of the dizi with measurements, both with and without a membrane. The TMM is used to generate the distribution map of normalized acoustic pressure and velocity along the main bore as a function of frequency for different fingerings. Different acoustic characteristics are discussed through the analysis of the pressure and flow maps. It is found that the four extra end holes compose a second tonehole lattice, which is independent of the one formed by the finger holes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of meetings on acousticsSame topicMusic Technology and Sound StudiesFrench-language works237,207