  Monitoring biodiversity at scale is challenging. Detecting and identifying
species in fine grained taxonomies requires highly accurate machine learning
(ML) methods. Training such models requires large high quality data sets. And
deploying these models to low power devices requires novel compression
techniques and model architectures. While species classification methods have
profited from novel data sets and advances in ML methods, in particular neural
networks, deploying these state of the art models to low power devices remains
difficult. Here we present a comprehensive empirical comparison of various
tinyML neural network architectures and compression techniques for species
classification. We focus on the example of bird song detection, more concretely
a data set curated for studying the corn bunting bird species. The data set is
released along with all code and experiments of this study. In our experiments
we compare predictive performance, memory and time complexity of classical
spectrogram based methods and recent approaches operating on raw audio signal.
Our results indicate that individual bird species can be robustly detected with
relatively simple architectures that can be readily deployed to low power
devices.
