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3GPP IVAS Codec – Perspectives on Development, Testing and Standardization

2025· article· en· W4408353769 on OpenAlexaff
Stefan Bruhn, Tomas Toftgard, Stefan Döhla, H-Y. Su, Laura Laaksonen, Toshio Moriya, Stéphane Ragot, H. Ehara, Marek Szczerba, Имре Вaргa, A. Schevciw, Milan Jelinek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsVoiceAge (Canada)
Fundersnot available
KeywordsStandardizationCodecComputer scienceOperating system

Abstract

fetched live from OpenAlex

The standardization of the codec for Immersive Voice and Audio Services (IVAS) was completed by the 3rd Generation Partnership Project in June 2024. The IVAS codec goes beyond traditional mono voice coding by representing and reproducing the spatial characteristics of sound, creating an immersive auditory experience. It opens the space for new applications in the realm of mobile communications and user-generated live content streaming such as immersive telephony and extended reality (XR) teleconferencing. The present paper provides a brief overview of the IVAS standard framework covering key features and properties and unique perspectives that are essential for understanding the underlying development and standardization processes that have led to this new 3GPP standard.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.316
Teacher spread0.280 · 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 designSimulation or modeling
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

Citations4
Published2025
Admission routes1
Has abstractyes

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