High-precision snore detection method based on deep learning

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a sleep-related respiratory disease, and sleep snoring is its most common and direct feature. However, the current snoring detection methods require a lot of medical manpower and medical equipment resources, resulting in many OSAHS patients cannot be treated in time. Therefore, this paper proposes a snore detection method based on deep learning and a snore dataset. The detection method first calculates the time-domain waveform, spectrogram, and Mel-spectrogram for each audio segment in the snore dataset. Then, the snore is recognized by convolution neural network. To better apply this method to mobile devices and intelligent devices, MobileNetV2 is selected as the detection network to classify snoring and non-snoring images. The experimental results show that the proposed method can accurately recognize snores with 95.00% accuracy. And the spectrogram can better reflect the difference between snoring and non-snoring images.