MétaCan
Menu
Back to cohort
Record W2130323514 · doi:10.1109/icassp.2006.1659945

Resynchronization of the Adaptive Codebook in a Constrained celp Codec After a Frame Erasure

2006· article· en· W2130323514 on OpenAlexaff
Mohamed Chibani, Rémi Lefebvre, Philippe Gournay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCode-excited linear predictionCodebookErasureComputer scienceCodecSpeech codingEncoderFrame (networking)Speech recognitionLinear predictive codingAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

The adaptive codebook used in CELP (code excited linear prediction) codecs to model the pitch excitation allows the attainment of a high quality of synthesized speech but introduces a strong inter-frame dependency and consequently causes error propagation in case of frame erasure. In a previous work we showed that the error propagation can be greatly reduced by constraining, at the encoder side, the innovative codebook to partially model the pitch excitation. In this paper we extend this work by exploiting, at the decoder side, the pitch-related information present in the innovative excitation to speed up the recovery of the decoder. The method consists in adequately shifting the last pitch pulse present within the corrupted adaptive codebook memory so that it is resynchronized with the excitation parameters of the frame that follows the erased one

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)0.003
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.211
Teacher spread0.205 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Data Compression TechniquesFrench-language works237,207