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. We evaluated the performance of our model on seven different datasets representing bird species from different geographical regions. In our experiments, the model showed excellent results, achieving an average AUROC of 0.82 and an average cmAP of 0.37 across the seven datasets, making it comparable to state-of-the-art black-box models for bird sound classification. Thus, this work demonstrates that even for the challenging task of bioacoustic bird classification, powerful yet interpretable deep learning models can be developed to provide valuable insights to domain experts.
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