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Record W2395899498

SPATOSC: PROVIDING ABSTRACTION FOR THE AUTHORING OF INTERACTIVE SPATIAL SUDIO EXPERIENCES

2012· article· en· W2395899498 on OpenAlexaff
Mike Wozniewski, Zack Settel, Alexandre Quessy, Tristan Matthews, Luc Courchesne

Bibliographic record

VenueThe Journal of the Abraham Lincoln Association · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsSpatializationComputer scienceMultimediaLoudspeakerSurround soundVisualizationHuman–computer interactionSound (geography)Artificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Creating an interactive project that deals with 3-D sound becomes difficult when it needs to run in multiple venues, due to a diversity of potential loudspeaker arrangements and spatialization systems. For effective exchange, there needs to be a standardized approach to audio spatialization with a unified, but abstract way of representing 3D audio scenes. Formats from the gaming and multimedia communities are appealing, but generally lack the features needed by artists working with interactive new media. Non-standard features need to be supported, including support for multiple listeners, complex sound directivity, integration with networked show control and sound synthesis applications, as well as support for the tuning spatial effects such as Doppler shift, distance attenuation and filtering. Our proposed solution is an open source C++ library called SpatOSC,1 which can be included in existing visualization engines, audio development environments, and plugins for digital audio workstations. Instead of expecting that everyone adopt a new spatial audio format, our library provides an immediate solution, with “translators” that handle conversion between representations. A number of translators are already supported, with an extensible architecture that allows others to be developed as needed.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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 designBench or experimental
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

Citations4
Published2012
Admission routes1
Has abstractyes

Explore more

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