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Record W2099008400 · doi:10.1109/iembs.2008.4649547

Modulation filtering for heart and lung sound separation from breath sound recordings

2008· article· en· W2099008400 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
TopicBlind Source Separation Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsHeart soundsModulation (music)Computer scienceFrequency modulationAcousticsSpeech recognitionSIGNAL (programming language)Representation (politics)Independent component analysisSound (geography)Audio signalArtificial intelligencePhysicsRadio frequencySpeech codingTelecommunications

Abstract

fetched live from OpenAlex

Separation of heart and lung sounds from breath sound recordings is a challenging task due to the temporal and spectral overlap of the two signals. In this paper, the use of a spectro-temporal representation to improve signal separation is investigated. The representation is obtained by means of a frequency decomposition (termed modulation frequency) of temporal trajectories of short-term spectral components. Experiments described herein suggest that improved separability of heart (HS) and lung sounds (LS) is attained in the modulation frequency domain. Bandpass and bandstop modulation filters are designed to separate HS and LS signals from breath sound recordings, respectively. Visual and auditory inspection, quantitative analysis, as well as algorithm execution time are used to assess algorithm performance. Log-spectral distances below 1 dB corroborate our listening test which found no audible artifacts in separated heart and lung sound signals.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.405

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 head0.035
GPT teacher head0.301
Teacher spread0.267 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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