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Record W2220262159

Spectrum Analysis and Directivity Pattern of a Transducer-Driven Conch Shell

2015· article· en· W2220262159 on OpenAlexaff
Rasoul Morteza Pouraghdam, Rama Bhat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsConchAcousticsShell (structure)LoudspeakerTransducerTimbreSpiral (railway)DirectivityHarmonicsMusical instrumentHarmonicEngineeringPhysicsGeologyElectrical engineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

The sound of a conch shell is very unique in nature and has been used in different cultures around the world, mainly in India, Greece and Japan. It was used to mark auspicious occasions, for signaling purposes and on rare occasions as a musical instrument. The conch shell is considered as one of the earliest “horn instruments” used by mankind. However studies on the acoustical properties of the instrument is scarcely found in literature. In order to shed more light on the nature of the conch shell sound, we obtained X-ray images of two different conch shells. The images were processed for analyzing the geometry of the spiral cavity. We tried finding a spiral geometry that best matches and describes the spiral cavity displayed in the X-ray scans. Furthermore, the directivity pattern of a conch shell driven by a small loudspeaker and an electro-pneumatic transducer are measured experimentally. It is found that the shell radiates sound uniformly in space at frequencies near the cavity’s resonance. The shell's sound spectra in three different excitation cases are also compared (lip excitation, loudspeaker and electro-pneumatic transducer). As expected, the sound spectrum of the lip-driven shell contains clear peaks at a fundamental frequency and its harmonics, giving the sound a musical tone. In contrast, the overtones of the loudspeaker and electro-pneumatic transducer driven shell are not harmonics of a fundamental frequency.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.240
Teacher spread0.219 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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