Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes

Marine soundscapes provide the opportunity to non-invasively learn about,
monitor, and conserve ecosystems. Some fishes produce sound in chorus,
often in association with mating, and there is much to learn about fish
choruses and the species producing them. Manually analyzing years of
acoustic data is increasingly unfeasible, and is especially challenging
with fish chorus, as multiple fish choruses can co-occur in time and
frequency and can overlap with vessel noise and other transient sounds.
SoundScape Learning (SSL) is a novel unsupervised automated method, to
separate fish chorus from soundscape. SSL is an integrated technique that
makes use of randomized robust principal component analysis (RRPCA),
unsupervised clustering, and a neural network. SSL was applied to 14
recording locations off southern and central California and was able to
detect a single fish chorus of interest in 5.3 yrs of acoustically diverse
soundscapes. Through application of SSL, the chorus of interest was found
to be nocturnal, increased in intensity at sunset and sunrise, and was
seasonally present from late Spring to late Fall. Further application of
SSL will improve understanding of fish behavior, essential habitat,
species distribution, and potential human and climate change impacts, and
thus allow for protection of vulnerable fish species. This repository
provides example data and code for the JASA paper: SoundScape
Learning: an automatic method for separating fish chorus in marine
soundscapes.