  Heart Sound (also known as phonocardiogram (PCG)) analysis is a popular way
that detects cardiovascular diseases (CVDs). Most PCG analysis uses supervised
way, which demands both normal and abnormal samples. This paper proposes a
method of unsupervised PCG analysis that uses beta variational auto-encoder
($\beta-\text{VAE}$) to model the normal PCG signals. The best performed model
reaches an AUC (Area Under Curve) value of 0.91 in ROC (Receiver Operating
Characteristic) test for PCG signals collected from the same source. Unlike
majority of $\beta-\text{VAE}$s that are used as generative models, the
best-performed $\beta-\text{VAE}$ has a $\beta$ value smaller than 1. Further
experiments then find that the introduction of a light weighted KL divergence
between distribution of latent space and normal distribution improves the
performance of anomaly PCG detection based on anomaly scores resulted by
reconstruction loss. The fact suggests that anomaly score based on
reconstruction loss may be better than anomaly scores based on latent vectors
of samples
