Research project
Full-night Tracheal Audio Event Modelling
Full-night acoustic representation learning for localising apnea–hypopnea events and estimating burden.
Problem
Short isolated clips can miss temporal context and event burden across a full sleep study.
Why it matters
Event-level localisation can make model outputs more interpretable and clinically aligned than only producing a single whole-night label.
Pipeline
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01
Full-night audio
02
Chunking & QC
03
SSL representation
04
Temporal localisation
05
Event burden
Signals
Tracheal audio
Models
Self-supervised speech/audio encodersTemporal event models