## 4 Discussion  
Parameters measured from calls of _P. auritus_ were different from those published using the same dataset [21]. This discrepancy likely resulted from a difference in the parameter measurement algorithm used in the two studies. In the previous study, when a jump in the frequency of the fundamental harmonic was detected, indicative of a switch in energy to a higher harmonic, the algorithm continued mapping the frequency-time course of the fundamental. In this study, such a jump would cause the call isolation algorithm to stop prematurely and return parameters representing only a part of the call. Regardless of the true values of call parameters, as long as values are measured consistently and differ from those of other species' calls, then the classifiers will be able to correctly identify this species. Such was the case here with more than 95% of _P. auritus_ calls classified correctly. However, parameters presented here should not be seen as accurately describing the calls of _P. auritus_. In addition to the five base parameters [21], this study extracted seven previously undescribed descriptors from the calls. Since the five base parameters measured from all but one species' calls were in agreement with those published previously [21], the increase in classification performance can be attributed to either the new parameters or the new classification algorithms (ENNs and SVM). Although the DFA in this study misclassified more calls than the previous study [21], a DFA using only the five base parameters resulted in a classification rate for species and genus 1% and 4% lower respectively ([PERSON], unpubl. data) when compared with the DFA when 9 parameters were included. This suggests that although the new parameters do help increase correct identification rates, the use of new classification algorithms had a greater effect. To provide consistent objective call measurements, and prevent the user adding variation to echolocation call parameters by subjective measurement, all parameter extraction algorithms used in this study were deterministic and return the same result from the same echolocation call recording regardless of the user. This is important when the classification techniques are trained on parameters extracted in a particular way with their own inherent biases. By using a common method of parameter extraction, the bias of parameter measurements remains the same as those parameters used to train the classifiers [22]. This consistency can increase the likelihood of obtaining an accurate classification of unseen calls [15]. Although often used in classification of calls from echolocating bats, we chose to not use interpulse interval as an additional parameter because it relies on knowing that two adjacent calls in a signal are from the same individual. This would require classification of the calls before measuring the interpulse interval. An extension to the algorithms used in this study could potentially use this measurement as a confirmation or augmentation of classification. The classification of echolocation calls is a complex task. Recordings of echolocation calls are often only partially accurate representations of the original call produced by the bat [23]. A multitude of environmental factors can cause variability and distortion in call recordings [23]. Robust machine learning techniques tolerant of noisy datasets are therefore desirable. More established techniques such as DFA have been shown to perform relatively poorly when classifying the calls of some species of British bats [21]. However more recent machine learning techniques such as SVM, neural networks, and ENN have demonstrated superior performance on highly noisy datasets [17, 24, 25, 26, 27]. Nevertheless, the performance of these classifiers still depends on both the quality and quantity of available data. The classification rates of calls achieved in this study using DFA was less accurate than those published previously [21].