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Record W2049126398 · doi:10.1162/comj_a_00167

The Spatial Sound Description Interchange Format: Principles, Specification, and Examples

2013· article· en· W2049126398 on OpenAlexfundno aff
Nils Peters, Trond Lossius, Jan C. Schacher

Bibliographic record

VenueComputer Music Journal · 2013
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsnot available
FundersCentre for Interdisciplinary Research in Music Media and Technology
KeywordsComputer scienceNamespaceRendering (computer graphics)ImplementationTerminologyScope (computer science)Programming languageHuman–computer interactionDatabaseComputer graphics (images)

Abstract

fetched live from OpenAlex

SpatDIF, the Spatial Sound Description Interchange Format, is an ongoing collaborative effort offering a semantic and syntactic specification for storing and transmitting spatial audio scene descriptions. The SpatDIF core is a lightweight minimal solution providing the most essential set of descriptors for spatial sound scenes. Additional descriptors are introduced as extensions, expanding the namespace and scope with respect to authoring, scene description, rendering, and reproduction of spatial sound. A general overview presents the principles informing the specification, as well as the structure and the terminology of the SpatDIF syntax. Two use cases exemplify SpatDIF's potential for pre-composed pieces as well as interactive installations, and several prototype implementations that have been developed show its real-life utility.

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.004
Scholarly communication0.0080.011
Open science0.0040.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0170.013

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 head0.080
GPT teacher head0.228
Teacher spread0.148 · 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 designNot applicable
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

Citations10
Published2013
Admission routes1
Has abstractyes

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