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Record W4288070505 · doi:10.18280/ts.390324

Hybrid Transform Based Speech Band Width Enhancement Using Data Hiding

2022· article· en· W4288070505 on OpenAlexvenueno aff
Sunil Kumar Koduri, Kishore Kumar T

Bibliographic record

VenueTraitement du signal · 2022
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSpeech recognitionTelephonyWideband audioSpeech codingBandwidth (computing)WidebandVoice activity detectionBandwidth extensionDiscrete cosine transformTelephone networkSpeech enhancementSpeech processingElectronic engineeringTelecommunicationsArtificial intelligenceEngineeringDigital audioBackground noiseAudio signal

Abstract

fetched live from OpenAlex

The limited narrow band (LNB) speech signal spread in the range of 300 to 3400Hz used in public switched telephone networks results in poor-quality telephony speech. Bandwidth extension techniques are performed to expand the frequency range from LNB speech to a clear wideband (CWB) speech signal range of 50Hz-7000Hz over existing public telephone networks. In this paper, a novel robust speech bandwidth extension algorithm by Discrete Wavelet Transform- Discrete Cosine Transform- Based Data Hiding (DWT-DCT-DH) Hybrid transform model was used to spread the out-of-band (3400Hz to 7000Hz) speech frequencies over the LNB speech. In this proposed technique the out-of-band speech frequencies are embedded in LNB speech and imperceptibly spread over the network. These Embedded out-of-band speech frequencies are recovered steadily at the receiver end to generate a restored CWB telephony speech of considerably better quality. The proposed technique simulation results show more intelligible and better-quality telephony speech generated compared to the other bandwidth extension techniques.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.652
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.298
Teacher spread0.239 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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