  We focus on automatic feature extraction for raw audio heartbeat sounds,
aimed at anomaly detection applications in healthcare. We learn features with
the help of an autoencoder composed by a 1D non-causal convolutional encoder
and a WaveNet decoder trained with a modified objective based on variational
inference, employing the Maximum Mean Discrepancy (MMD). Moreover we model the
latent distribution using a Gaussian chain graphical model to capture temporal
correlations which characterize the encoded signals. After training the
autoencoder on the reconstruction task in a unsupervised manner, we test the
significance of the learned latent representations by training an SVM to
predict anomalies. We evaluate the methods on a problem proposed by the PASCAL
Classifying Heart Sounds Challenge and we compare with results in the
literature.
