Abstract
Snoring is a typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), and it is a widespread sleep disorder. Accurate detection of snoring can help to screen and diagnose OSAHS. However, the current voice recognition methods based on deep learning can not achieve satisfactory results. To accurately identify snoring, this paper proposes an automatic snoring detection method based on a convolutional neural network (CNN) and constructs a snore dataset. For each sound segment in the snoring dataset, we calculated the time-domain waveform, spectrogram, and Melspectrogram. The proposed method classifies snoring and non-snoring sound segment images through a new convolutional neural network MBAM-ResNet to accurately identify snoring. Experimental results show that spectrogram can better reflect the difference between snoring and non-snoring images and the accuracy of the proposed network for snoring on the spectrogram is 91.11%.
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