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Record W1775861028

An acoustical study of IPOD output: Effects of headsets and control settings

2006· article· en· W1775861028 on OpenAlexaffvenue
Desiree Pereira, Shaun Sharma, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueCanadian acoustics · 2006
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHeadphonesEqualizerAcousticsRange (aeronautics)Sound energyComputer scienceEnergy (signal processing)Speech recognitionEngineeringSound (geography)MathematicsTelecommunicationsStatisticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

An acoustical study was conducted to investigate the level of sound output of Apple iPod using different kinds of headsets and how output depends on factors such as equalizer and volume control settings. Three factors that differentiate one set of headphones from another are sensitivity, impedance, and frequency response. The acoustical output measurements were compared for samples of two genres of music played at four volume settings and different equalizer settings for three different headsets. Two 30 second sound clips were used for the tests, one from the Hip hop genre and the other from Electronica. Hip Hop songs are known for their strong precussion, thus most of their energy is concentrated in the low-frequency range between 1 and 4 KHz whereas Electronica song features synthetically produced sounds spanning a frequency range from 1 to 12 KHz. The largest and smallest differences at each volume level were used to determine which equalizer settings made the most differences. These physical measurements can be used to estimate the risk to human hearing.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.312
Teacher spread0.304 · 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 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

Citations1
Published2006
Admission routes2
Has abstractyes

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