Snoring Automatic Recognition and Classification Method Based on Bone-Conduction Microphone Data

Snoring is a common sleep-related phenomenon and is relevant to research on sleep-disordered breathing. Automatic snoring recognition may provide useful acoustic information for sleep breathing monitoring. In this study, sleep breathing sounds were recorded using a bone-conduction microphone attached to the skin over the throat. The recordings were manually annotated using Praat with five acoustic labels: snoring, breathing, apnea, speech, and noise. Segments labeled as speech or noise were excluded from the classification experiment, which focused on snoring, breathing, and apnea. Mel-frequency cepstral coefficients (MFCCs) were extracted from the retained recordings, and a hidden Markov model (HMM) implemented using the Hidden Markov Toolkit (HTK) was used to recognize sequences of breathing, snoring, and apnea labels. Using MFCC feature vectors with 24 dimensions and an HMM with 10 states, the exploratory analysis yielded 89.47% HTK word correctness and 28.95% HTK word accuracy on the same test data used for parameter selection. These exploratory results describe recognition of manually annotated acoustic categories in selected recordings. Further independent evaluation is required to assess generalizability and practical reliability.