Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions
This work addresses the need for reliable individual animal identification in zoology and ecology, though it is incremental as it builds on existing methods with new evaluation techniques.
The authors tackled the problem of automatically identifying individual animals from their vocalizations across different species and recording conditions, and introduced new analysis techniques to detect and reduce experimental confounds, improving classifier robustness.
Many animals emit vocal sounds which, independently from the sounds' function, embed some individually-distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here we present a general automatic identification method, that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance development of methods and comparisons of results.