Multi-class, unsupervised detection and classification of biological and anthropogenic sounds in coral reefs

Analyzing the complex and diverse soundscapes of ecosystems such as coral reefs remains a challenge for understanding environmental dynamics and processes. While machine learning techniques can significantly improve detection and classification capabilities, applications of traditional supervised learning to underwater acoustics are limited by the size and class-coverage of labeled datasets. Unsupervised machine learning offers the potential to detect and classify sounds without the guidance of human labels, including signals that were unknown to the human analyst. However, the majority of previously developed unsupervised approaches characterize reef soundscapes from correlative metrics without identifying specific sounds, and the few that detect individual signals have been trained on limited data (<10 days), which constrains the potential to generalize across datasets and geographical localities. Here, a convolutional autoencoder was built and trained on year-long acoustic datasets from four Hawaiian coral reefs, and latent embeddings were clustered using Gaussian mixture modeling. A total of 29 classes were automatically generated, and a manual review of samples in each class determined that nine of the classes corresponded to distinct biological and anthropogenic sounds. The classes were identified to be two call types from the damselfish, Dascyllus albisella, parrotfish feeding sounds, holocentrid calls, an unidentified fish sound, three humpback whale song units, and ship noise. The classifier was found to be robust against an independently-collected test dataset with D. albisella calls (AUC = 0.9) with no extra training on the labels. Diel, lunar, and seasonal trends were observed for all nine classes, including previously-unidentified responses of the holocentrid and unknown fish groups to lunar illumination. This work demonstrates the capability of unsupervised algorithms to cluster acoustic signals into identifiable biological and anthropogenic categories in order to examine and characterize ecological trends.