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Many animals emit vocal sounds which, independently from the sounds'function, embed some individually-distinctive signature. Thus the automaticrecognition of individuals by sound is a potentially powerful tool for zoologyand ecology research and practical monitoring. Here we present a generalautomatic identification method, that can work across multiple animal specieswith various levels of complexity in their communication systems. We furtherintroduce new analysis techniques based on dataset manipulations that canevaluate the robustness and generality of a classifier. By using thesetechniques we confirmed the presence of experimental confounds in situationsresembling those from past studies. We introduce data manipulations that canreduce the impact of these confounds, compatible with any classifier. Wesuggest that assessment of confounds should become a standard part of futurestudies to ensure they do not report over-optimistic results. We provideannotated recordings used for analyses along with this study and we call fordataset sharing to be a common practice to enhance development of methods andcomparisons of results.

Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions