Sleep Snoring Sound Recognition Based on Wavelet Packet Transform

Abstract

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.

Keywords

• snoring recognition
• wavelet packet transform
• feature selection
• machine learning