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Reconstructing audio signals from modified non-coherent hilbert envelopes

2007· article· en· W182481410 on OpenAlexaff
Joachim Thiemann, P. Kabal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpeech codingSpeech recognitionComputer scienceAudio signalSub-band codingSpectral envelopeBasilar membraneCoding (social sciences)Filter bankAcousticsFilter (signal processing)MathematicsComputer visionPhysics

Abstract

fetched live from OpenAlex

In this paper, we present a speech and audio analysis-synthesis method based on a Basilar Membrane (BM) model. The audio signal is represented in this method by the Hilbert envelopes of the responses to complex gammatone filters uniformally spaced on a critical band scale. We show that for speech and audio signals, a perceptually equivalent signal can be reconstructed from the envelopes alone by an iterative procedure that estimates the associated carrier for the envelopes. The rate requirement of the envelope information is reduced by low-pass filtering and sampling, and it is shown that it is possible to recover a signal without audible distortion from the sampled envelopes. This may lead to improved perceptual coding methods. Index Terms: speech and audio coding, auditory representation, auditory model inversion

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.047
GPT teacher head0.290
Teacher spread0.243 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2007
Admission routes1
Has abstractyes

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