As part of the George B. Moody PhysioNet Challenge 2022, we developed a computational approach to identify abnormal cardiac function from phonocardiograms that combines deep learning and traditional machine learning methods. We adopted a supervised contrastive learning and a deep convolutional neural network to obtain an embedding of the phonocardiogram slice onto a unit hypersphere in low-dimensional space. Thus, we applied the obtained latent factors to classify patients using a Random Forest model. The murmur detection classifier created by our team CeZIS received a weighted accuracy score of 0.796 (ranked 3rd out of 305 submissions) and Challenge cost score of 9479 (ranked 22nd out of 305 submissions) on the hidden validation set.