An Auto Encoder For Audio Dolphin Communication
This work addresses the need for efficient analysis in dolphin communication research, but it is incremental as it applies existing deep learning methods to a new domain.
The authors tackled the problem of automating the analysis of audible dolphin signals, which is manually cumbersome, by proposing an unsupervised autoencoder model that learns embeddings for clustering, signal detection, and classification.
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.