7th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2011)
In this work, an automatic snore detection
system of acoustic snoring signals has been designed.
its purpose is to assist an alternative non-invasive
method for diagnosing obstructive sleep apnea (osa)
based on acoustic signal processing. the detector is
based on Gaussian mixture models that were trained
and validated on full night acoustic signals that were
recorded from a sleep laboratory, along with
polysomnographic tests taken from patients with
widely distributed severity of osa. the snore
detection system includes steps from noise reduction
through event detection and all the way to snore
In order to analyze the performance of our proposed detector, a total of more than 80,000 acoustic episodes from 33 different osa patients were manually segmented into snore and non-snore episodes; among the non-snore episodes we can find a variety of sleep related noises such as blanket and pillow murmurs, moaning, groaning, coughing, and talking. the validation dataset was recorded using two different audio recorders to ensure the robustness of the detector.
The events' total identification rate was 97.12% with 96.02% positive detection of snore as snore (sensitivity) and 97.90% detection of noise as noise (specificity).
Index Terms. obstructive sleep apnea, snore detection, GMM
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Dafna, E. / Tarasiuk, A. / Zigel, Yaniv (2011): "Automatic detection of snoring events using Gaussian mixture models", In MAVEBA-2011, 17-20.