Snoring Recognition Deep Learning Method Based on Transfer Learning and Spatio-Temporal Feature Extraction

Obstructive sleep apnea (OSA) is a common sleep disorder that is closely associated with snoring patterns. Since acoustic monitoring provides a non-invasive and easy-to-use method for detecting sleep abnormalities, this study presents a YAMNet-based hierarchical attention framework for classifying nocturnal audio. The proposed method uses a pre-trained YAMNet architecture to automatically extract Mel-spectrograms features, and further converts the raw audio into a three-channel spectral representation. After the above processing, the features are fed into a classification network that integrates dual convolutional layers, an attention mechanism, and gated recurrent units for hierarchical feature processing. This two-stage process starts with the extraction of distinctive snores and then applies spatial channel attention and temporal modelling for accurate classification. Nocturnal sounds are classified into three categories: respiratory events, OSA-related snoring, and ambient noise. Experimental results show that the method achieves 96.7% accuracy in detecting snoring on the training set of data and 98.30% average accuracy on the test set, which is significantly better than traditional methods. This non-invasive monitoring tool can help patients understand their sleep health and support health management and disease prevention.