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Record W2159615724 · doi:10.1109/vetec.1989.40140

Performance of a low complexity CELP speech coder under mobile channel fading conditions

2003· article· en· W2159615724 on OpenAlexaff
W.P. LeBlanc, S.A. Hanna, S. Mahmoud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsCarleton University
FundersInstituto de Telecomunicações
KeywordsCode-excited linear predictionComputer scienceSpeech codingCodecCodebookVector sum excited linear predictionConvolutional codeResidualSpeech recognitionCodec2Full RateCoding (social sciences)Adaptive Multi-Rate audio codecLinear predictive codingComputer engineeringComputer hardwareVoice activity detectionAlgorithmSpeech processingDecoding methods

Abstract

fetched live from OpenAlex

The authors report on the simulation of a voice codec intended for the terrestrial and satellite radio environments. The coder is based on a complexity reduced version of code-excited linear prediction (CELP). The codebook search complexity has been reduced to only 0.5 MFLOPS (million floating point operations per second) while maintaining excellent speech quality. Novel methods to quantize the residual and the long- and short-term model filters are presented. Due to its inherent simplicity, the codec can be easily realized in VLSI form or implemented on a dedicated single-chip digital signal processor. The coder has been tested under simulated channel conditions. Different block coding and convolutional coding techniques have been applied to improve the performance of the coder in the presence of burst errors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations1
Published2003
Admission routes1
Has abstractyes

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