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Record W2116820729 · doi:10.1109/icassp.2014.6854287

New bit-plane probability calculations for Scalable to Lossless Audio Coding

2014· article· en· W2116820729 on OpenAlexaff
Mahmood Movassagh, P. Kabal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsBit planeComputer scienceSub-band codingAlgorithmClipping (morphology)Quantization (signal processing)Harmonic Vector Excitation CodingCoding (social sciences)Lossless compressionCoding gainTunstall codingResidualData compressionContext-adaptive binary arithmetic codingTransform codingSpeech codingTheoretical computer scienceSpeech recognitionMathematicsDiscrete cosine transformDecoding methodsArtificial intelligenceStatisticsBit fieldEngineering

Abstract

fetched live from OpenAlex

Considering the properties of the residual signal, core-based bit-plane probabilities are provided for MPEG-4 Audio Scalable to Lossless Coding (SLS), which matches the quantization and coding performed in the core layer. Using the same strategy, new probabilities are obtained to consider the clipping effect in bit-plane coding of an unbounded signal, which is useful for non-core mode of SLS coding. Simulations show that considering the core layer parameters and the clipping effect improve the bit-plane probabilities estimation compared to the existing method.

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: Methods
Teacher disagreement score0.501
Threshold uncertainty score0.396

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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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