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Object-Based Audio Coding in Immersive Mobile Communications

2025· article· W4417402951 on OpenAlexaff
Václav Eksler, Milan Jelinek

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsQ & T ResearchUniversité de SherbrookeVoiceAge (Canada)
Fundersnot available
KeywordsCodecAudio over EthernetDigital audioCoding (social sciences)Speech codingWideband audioDigital audio broadcastingAdaptive Multi-Rate audio codec

Abstract

fetched live from OpenAlex

Object-based audio is a spatial audio representation in which all individual sounds are distributed independently and accompanied by metadata. The sounds are then mixed during reproduction, providing a flexible and personalized immersive audio experience. Object-based audio can be found in many audio reproduction, streaming, or broadcasting systems. However, its use in interactive communications is a challenge as it faces many constraints like limited transmission bitrate, low delay, limited computational and memory resources, packet losses, or support of discontinuous transmission. This paper discusses trade-offs to overcome these constraints and presents novel methods that enable the use of object-based audio in modern 5G mobile communications services. These methods have been adopted in the recently standardized 3GPP codec for Immersive Voice and Audio Services (IVAS). Several implementation and performance details of IVAS object-based audio coding are also provided.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
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 score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0050.004
Research integrity0.0000.001
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 head0.022
GPT teacher head0.340
Teacher spread0.318 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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