Acoustic Species at NeurIPS 2021

On Tuesday December 14th, 2021 Jacob Ayers and Mugen Blue of the Automated Acoustic Species Identification team attended the NeurIPS 2021 workshop dedicated to “Tackling Climate Change with Machine Learning”. During this workshop they talked with various members of the machine learning community about their paper: “Reducing the Barriers of Acquiring Ground-truth from Biodiversity Rich Audio Datasets Using Intelligent Sampling Techniques”. The paper tackled the challenges the team faced when providing completely unlabeled audio data from their Scripps Coastal Reserve Biodiversity Trail AudioMoth deployment to citizen scientists familiar with bird species in the region. Specifically, the team found that they would accidentally provide audio clips without bird vocalizations to be labeled, consequently wasting time and resources. The NeurIPS paper covers a couple different methods used by the species identification team to use Deep Learning Neural Network models trained in binary bird classification to increase the probability that any clip provided to the volunteers, would in fact, have bird vocalizations within them to label. If interested, the paper and relevant workshop information can be found at this link: