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Record W2789765466 · doi:10.5281/zenodo.1176227

Performance With An Electronically Excited Didgeridoo

2017· article· en· W2789765466 on OpenAlexaff
Abram Hindle, Daryl Posnett

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExcited stateComputer sciencePhysicsAtomic physics

Abstract

fetched live from OpenAlex

The didgeridoo is a wind instrument composed of a single large tube often used as drone instrument for backing up the mids and lows of an ensemble. A didgeridoo is played by buzzing the lips and blowing air into the didgeridoo. To play a didgeridoo continously one can employ circular breathing but the volume of air required poses a real challenge to novice players. In this paper we replace the expense of circular breathing and lip buzzing with electronic excitation, thus creating an electro-acoustic didgeridoo or electronic didgeridoo. Thus we describe the didgeridoo excitation signal, how to replicate it, and the hardware necessary to make an electro-acoustic didgeridoo driven by speakers and controllable from a computer. To properly drive the didgeridoo we rely upon 4th-order ported bandpass speaker boxes to help guide our excitation signals into an attached acoustic didgeridoo. The results somewhat replicate human didgeridoo playing, enabling a new kind of mid to low electro-acoustic accompaniment without the need for circular breathing.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.998

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 studies0.0050.000
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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.

Study designOther design
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
Published2017
Admission routes1
Has abstractyes

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