Improving Sleep Quality Assessment through Clustering and Data Augmentation of Sleep Sound Events

The proposed method achieves high accuracy in classifying sleep satisfaction by clustering sleep sound events, using the cluster membership probabilities as input features, and applying data augmentation to increase the training data. The method also provides interpretable insights into important sleep sound events and individual-specific characteristics that influence sleep quality.