  To protect tropical forest biodiversity, we need to be able to detect it
reliably, cheaply, and at scale. Automated species detection from passively
recorded soundscapes via machine-learning approaches is a promising technique
towards this goal, but it is constrained by the necessity of large training
data sets. Using soundscapes from a tropical forest in Borneo and a
Convolutional Neural Network model (CNN) created with transfer learning, we
investigate i) the minimum viable training data set size for accurate
prediction of call types ('sonotypes'), and ii) the extent to which data
augmentation can overcome the issue of small training data sets. We found that
even relatively high sample sizes (> 80 per call type) lead to mediocre
accuracy, which however improves significantly with data augmentation,
including at extremely small sample sizes, regardless of taxonomic group or
call characteristics. Our results suggest that transfer learning and data
augmentation can make the use of CNNs to classify species' vocalizations
feasible even for small soundscape-based projects with many rare species.
Retraining our open-source model requires only basic programming skills which
makes it possible for individual conservation initiatives to match their local
context, in order to enable more evidence-informed management of biodiversity.
