MétaCan
Menu
Back to cohort

A novel frequency domain BWE with relaxed synchronization and associated BWE switching

2015· article· en· W2288973801 on OpenAlexaff
Lei Miao, Zexin Liu, Xingtao Zhang, Chen Hu, Jon Gibbs, Kihyun Choo, Eunmi Oh, Václav Eksler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsVoiceAge (Canada)
Fundersnot available
KeywordsComputer scienceCodecAlgorithmSynchronization (alternating current)Coding (social sciences)Frequency domainSpeech codingBandwidth extensionReal-time computingSpeech recognitionComputer networkTelecommunicationsMathematicsComputer vision

Abstract

fetched live from OpenAlex

This paper presents a novel frequency domain bandwidth extension (BWE) scheme with relaxed synchronization, optimized for coding inactive and music/mixed content signals. The algorithm achieves high subjective quality at low and medium bitrates and it has a low algorithmic delay. The algorithm is part of the 3GPP Enhanced Voice Services (EVS) codec. In addition to the presented algorithm, the EVS codec employs also a time domain BWE scheme optimized for active speech coding. Consequently a seamless switching between these BWE technologies is required and described in this paper as well.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Data Compression TechniquesFrench-language works237,207