Unsupervised detection and classification of fish vocalizations in coral reefs

Typical convolutional neural network detectors for acoustic signals rely on large quantities of labeled data, which can be expensive and time-intensive to generate. Unsupervised neural network architectures allow for the automatic detection and classification of acoustic signals, which may be especially useful for classifying underwater signals that a typical human labeler may not be familiar with. Here, we used a convolutional autoencoder to automatically detect and classify fish vocalizations in a coral reef off the coast of Hawaii Island. A database of more than 2 million spectrogram segments were generated with durations of one second and frequency ranges of 30–700 Hz, corresponding to the timescale and spectrum of a significant portion of fish calls. The autoencoder was successful in recreating the features of input spectrograms, and k-means clustering was applied to the latent feature space of each sample to automatically generate classes. Visual inspection of example spectrograms within each class confirms that the autoencoder was successful in classifying fish calls, including differentiating between separate call types.