  In this paper, we adapt Recurrent Neural Networks with Stochastic Layers,
which are the state-of-the-art for generating text, music and speech, to the
problem of acoustic novelty detection. By integrating uncertainty into the
hidden states, this type of network is able to learn the distribution of
complex sequences. Because the learned distribution can be calculated
explicitly in terms of probability, we can evaluate how likely an observation
is then detect low-probability events as novel. The model is robust, highly
unsupervised, end-to-end and requires minimum preprocessing, feature
engineering or hyperparameter tuning. An experiment on a benchmark dataset
shows that our model outperforms the state-of-the-art acoustic novelty
detectors.
