  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.
