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.
Illegal construction should be detected as early as possible as it can damage the environment and economy. However, the existing methods for detecting illegal construction can be improved in terms of their detection cycles, accuracy, and speed. Moreover, there are relatively few valuable real-world image datasets for detecting illegal construction. To address these issues, a high-precision real-time detection model named YEMNet and a new large-scale dataset for detection of illegal construction objects (ICOS) are proposed herein. Our YEMNet is based on the You Only Look Once v3 object detection model; this model adopts a lightweight convolutional neural network called “EfficientNet” as the backbone for feature extraction. Then, YEMNet employs a new activation function Mish outside the backbone to achieve efficient optimization and strong generalization, thereby improving the recognition accuracy for ICOS in complicated scenes. Our proposed dataset comprises 15 categories and 13,701 photographs of ICOS captured under different conditions concerning weather, lighting, and natural scenes. Extensive experiments on the proposed dataset show that YEMNet achieves a mean average precision of 91.41% with fewer parameters, thereby outperforming state-of-the-art object detectors. Our dataset and code are available at https://github.com/king-king-king/ICOS-Dataset.
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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