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Record W1943963003 · doi:10.1109/icassp.1984.1172363

A new concept for encoding speech amplitude time quantization

2005· article· en· W1943963003 on OpenAlexaff
J. Soumagne, J.-P. Adoul, S. Morissette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceSpeech codingQuantization (signal processing)Analog signalAnalog transmissionCodec2Linear predictive codingSub-band codingAmplitudeSpeech recognitionSampling (signal processing)AlgorithmTransmission (telecommunications)TelecommunicationsPhysicsOptics

Abstract

fetched live from OpenAlex

For digital modulations applied to the coding of speech signals, a fixed sampling and transmission rate is always chosen. For commercial telephone these rates are respectively, 8 KHz and 64 Kbits/sec, corresponding to a filtered signal bandwidth of 300 to 3300 Hz. A new processing concept (variable sampling and digital quantization) is proposed where a sample is coded with a single binary word. The code word corresponds to an information pair: a variable and adaptive sampling time and a coding angle associated to the signal. The basic principle is conceived around a distribution of the coded samples in amplitude and time (amplitude-time coding) along an adaptive coding curve associated to each coded/decoded sample of the original signal. A variable sampling rate requires a buffer, thus a delay, for transmission at a fixed rate. The transmitted signal so obtained is a transposition of the original speech signal and consequently its characteristics (bandwith, amplitude dynamic range) are modified. Some of the characteristics of the transmitted signal are ultimately used for the digital or even the analog transmission of the signal.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.581
Threshold uncertainty score0.342

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 head0.022
GPT teacher head0.309
Teacher spread0.287 · 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 designOther design
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
Published2005
Admission routes1
Has abstractyes

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