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Narrowband perceptual audio coding: enhancements for speech

2001· article· en· W195152713 on OpenAlexaff
Hossein Najaf-Zadeh, P. Kabal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsMcGill University
Fundersnot available
KeywordsNarrowbandSpeech codingSpeech recognitionComputer scienceCoding (social sciences)PerceptionSub-band codingAdaptive Multi-Rate audio codecSpeech processingVoice activity detectionTelecommunicationsPsychologyMathematics

Abstract

fetched live from OpenAlex

This paper presents a bi-modal coding paradigm to com-press narrowband audio signals at 8 kbit/s. In the general mode, the Enhanced Narrowband Audio Coder (ENPAC) exploits the characteristics of the human hearing system to adaptively code the perceptually important spectral components of the input audio. The other mode is em-ployed to handle audio inputs with a strong harmonic structure. In that mode, the input block is represented by its audible harmonics. The spectral magnitude is mod-eled by the linear prediction analysis in the time domain. The phase of each harmonic is predicted and the phase residues are quantized using an adaptive bit allocation algorithm. This paper introduces a perceptually-based upper bound for phase errors of spectral components. The ENPAC encoder delivers good quality for narrow-band speech and non-speech inputs. 1

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.385

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 communication0.0000.000
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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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 designBench or experimental
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
Published2001
Admission routes1
Has abstractyes

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