## 3 Results
achieving the highest correct identification rate) SVM for classifying calls to genus had a correct classification rate of 93% (Table 4). All calls from _Barbastella_ were correctly identified, and calls from _Myotis_, _Pipistrellus_, _Plecotus_, and _Rhinolophus_ were all identified correctly with more than 90% accuracy. Calls from _Eptesicus_ were classified relatively poorly (79%) and were most often confused with calls from _Nyctalus_ species (16% of calls). Out of all calls, only one call from _Rhinolophus_ was unclassified. The measured duration of this call was lower than the actual call duration (as determined by visual inspection of a spectrogram of the call), which caused the start frequency of the call to be measured higher than the expected value. The call type parameter also classed this call as kinked. The best SVM for classifying calls to species achieved an 87% correct classification rate (Table 5), with calls from _R. ferrumequinum_ being classified correctly 100% of the time. Calls from _B. barbastellus_, _M. nattereri_, _P. apirutus_, _P. pipistrellus_, _P. pygmaeus_, and _R. hipposideros_ were all classified with greater than 90% accuracy. Calls from _M. mystacinus_ were the least accurately identified with just 64% classified correctly. Calls from this species were misclassified as being from _M. brandtii_ (17% of calls) and _M. nattereri_ (8% of calls). Calls from _N. leisleri_ were also identified poorly with 71% correct classification. Calls were misclassified as being from _E. serotinus_ (11% of calls) and _N. noctula_ (10% of calls). Eighty percent of calls from _M. daubentonii_ were classified correctly while 16% were unable to be classified. A single call from _R. hipposideros_ was unclassified. This was the same call that could not be classified by the support vector machine classifying calls to genus. All ENNs consisted of 21 neural networks, as there were sufficient networks that classified with greater than 50% accuracy. The ENN trained to classify calls to genus yielded a 98% correct classification rate (Table 4). Calls from _Barbastella_, _Myotis_, _Pipistrellus_, and _Rhinolophus_ were all \begin{table}
\begin{tabular}{l c c c c} Parameter & Genus(1) & Genus(2) & Species(1) & Species(2) \\ \hline End frequency & 0.197 & 0.213 & 0.147 & 0.126 \\ Start frequency & 0.035 & 0.221 & 0.153 & 0.139 \\ Centre frequency & 0.514 & 0.214 & 0.153 & 0.147 \\ Duration & 0.002 & 0.345 & 0.163 & 0.221 \\ Frequency of maximum energy & 0.835 & 0.262 & 0.18 & 0.2 \\ Standardised Bandwidth & 0.97 & & 0.44 & \\ Call type & 1 & & 0.505 & \\ Quartile 4 & 1 & & 0.551 & \\ Quartile 2 & 1 & & 0.579 & \\ Quartile 1 & 1 & & 0.64 & \\ Quartile 3 & 1 & & 0.699 & \\ RoC & 1 & & 0.982 & \\ \end{tabular}
\end{table} 
Table 6: Comparison of [PERSON] lambda values between this study (1) and a previous study [18] (2) for each call parameter used in the DFA to classify calls to genus and species. A lower Lambda indicates a higher importance of a parameter for classification. classified with 100% accuracy. Calls from all other genera were classified with more than 90% accuracy. The lowest rate of correct classification was for calls from the genus _Nyctalus_ (95%); which were confused with calls of _Eptesicus_. Classification of calls to species achieved 98% accuracy (Table 5). Calls from _B. barbastellus_, _M. bechsteinii_, _M. daubentonii_, both _Pipistrellus_ species and both _Rhinolophus_ species were all classified with 100% accuracy. Calls from all other species were classified with more than 90% accuracy. Calls from _N. leisleri_ were 91% classified correctly, and were misclassified as calls of _E. serotinus_ (5%), and _N. noctula_ (4%). The hierarchical ensemble of the best performing neural networks achieved an overall accuracy of 98% (Table 5). Each ensemble of species classifiers within the _Myotis_, _Nyctalus_, _Pipistrellus_, and _Rhinolophus_ genera consisted of 21 neural networks. Echolocation calls from _B. barbastellus_, _M. bechsteinii_, _M. nattereri_, _P. pipistrellus_, _P. pygmaeus_, _R.