  Monitoring of bird populations has played a vital role in conservation
efforts and in understanding biodiversity loss. The automation of this process
has been facilitated by both sensing technologies, such as passive acoustic
monitoring, and accompanying analytical tools, such as deep learning. However,
machine learning models frequently have difficulty generalizing to examples not
encountered in the training data. In our work, we present a semi-supervised
approach to identify characteristic calls and environmental noise. We utilize
several methods to learn a latent representation of audio samples, including a
convolutional autoencoder and two pre-trained networks, and group the resulting
embeddings for a domain expert to identify cluster labels. We show that our
approach can improve classification precision and provide insight into the
latent structure of environmental acoustic datasets.
