AudioProtoPNet: An interpretable deep learning model for bird sound
  classification
René Heinrich Bernhard Sick Christoph Scholz
Recently, scientists have proposed several deep learning models to monitor
the diversity of bird species. These models can detect bird species with high
accuracy by analyzing acoustic signals. However, traditional deep learning
algorithms are black-box models that provide no insight into their
decision-making process. For domain experts, such as ornithologists, it is
crucial that these models are not only efficient, but also interpretable in
order to be used as assistive tools. In this study, we present an adaption of
the Prototypical Part Network (ProtoPNet) for audio classification that
provides inherent interpretability through its model architecture. Our approach
is based on a ConvNeXt backbone architecture for feature extraction and learns
prototypical patterns for each bird species using spectrograms of the training
data. Classification of new data is done by comparison with these prototypes in
latent space, which simultaneously serve as easily understandable explanations
for the model's decisions.