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Record W2158144386 · doi:10.1109/icassp.2004.1326085

Combining equalization and estimation for bandwidth extension of narrowband speech

2004· article· en· W2158144386 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsNarrowbandBandwidth extensionBandwidth (computing)Computer scienceEqualization (audio)Extension (predicate logic)Speech recognitionTelecommunicationsSpeech codingDecoding methodsAudio signal

Abstract

fetched live from OpenAlex

Current public telephone networks compromise voice quality by bandlimiting the speech signal. Telephone speech is characterized by a bandpass response from 300 to 3400 Hz. The voice quality is perceived as being much worse than for wideband speech (50-7000 Hz). We present a novel approach which combines equalization and estimation to create a wideband signal, with reconstructed components in the 3400 Hz to 7000 Hz range. Equalization is used in the 3400-4000 Hz range. Its performance is better than statistical estimation procedures, because the mutual dependencies between the narrowband and highband parameters are not sufficiently large. Subjective evaluation using an improvement category rating shows that the reconstructed wideband speech using both equalization and estimation substantially enhances the quality of telephone speech. We have also evaluated the performance on the narrowband output of several standard codecs. Overall, the use of equalization for part of the highband regeneration makes the system more robust to phonetic variability and speaker gender.

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.

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 score0.466
Threshold uncertainty score0.219

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 science0.0000.000
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 head0.023
GPT teacher head0.308
Teacher spread0.284 · 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

Quick stats

Citations27
Published2004
Admission routes1
Has abstractyes

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