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Self-Supervised Learning for Automated Species Detection from Passively Recorded Soundscapes in Avian Diversity Monitoring
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DescriptionBy detecting different animal species reliably at scale we can protect biodiversity. Yet, traditionally, biodiversity data has been collected by expert observers which is prohibitively expensive, not reliable neither scalable. Automated species detection via machine-learning is promising, but it is constrained by the necessity of large training data sets all labeled by human experts. Here, we propose to use Self-Supervised Learning for studying semantic features from passively collected acoustic data. We utilized a joint embedding configuration to acquire features from spectrograms. We processed recordings from ∼190 hours of audio. In order to process these volumes of data we utilized a HPC cluster provided by the Argonne Leadership Computing Facility. We analyzed the output space from a trained backbone which highlights important semantic attributes of the spectrograms. We envisage these preliminary results as compelling for future automatic assistance of biologist as a pre-processing stage for labeling very big data sets.