  Research in dolphin communication and cognition requires detailed inspection
of audible dolphin signals. The manual analysis of these signals is cumbersome
and time-consuming. We seek to automate parts of the analysis using modern deep
learning methods. We propose to learn an autoencoder constructed from
convolutional and recurrent layers trained in an unsupervised fashion. The
resulting model embeds patterns in audible dolphin communication. In several
experiments, we show that the embeddings can be used for clustering as well as
signal detection and signal type classification.
