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Record W2091846282 · doi:10.1121/1.2932618

Input impedance measurements of conical acoustic systems using the two-microphone technique

2008· article· en· W2091846282 on OpenAlexaff
Antoine Lefebvre, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsMcGill University
Fundersnot available
KeywordsAcousticsMicrophoneConical surfaceElectrical impedanceDistortion (music)Computer scienceSIGNAL (programming language)Acoustic impedanceSystem of measurementNoise (video)Transmission (telecommunications)Materials sciencePhysicsTelecommunicationsSound pressureArtificial intelligence

Abstract

fetched live from OpenAlex

The two-microphone technique with a broadband excitation has been used for the measurement of various objects including straight conical waveguides and alto saxophones. An improved procedure has been developed whereby the noise excitation signal is pre-filtered by the inverse frequency response of the system under consideration. This approach helps reduce distortion and improves the SNR of the measurement. Results with and without pre-filtering are compared for different fingerings of an alto saxophone. The input impedance of a straight conical waveguide is compared with theory and discrepancies are analyzed. The input impedance of saxophones are evaluated with the transmission matrix approach and compared with measurements. A software environment for efficient comparison and analysis of measurement data with theoretical calculations is presented. We also propose a framework for sharing raw measurement data among researchers to allow comparison of results obtained with different apparatus on similar objects to better quantify their accuracy.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.280
Teacher spread0.243 · 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
GenreMethods

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

Citations1
Published2008
Admission routes1
Has abstractyes

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