## 3 Experiments
### Asian Elephant (Elephas maximus): Repertoire Analysis
One Hidden Markov Model is used to model each of the 11 different call types discussed above, with 12 emitting states per HMM and GMM state distributions with three Gaussian mixtures per state. A range of parameter variations was also implemented for comparative evaluation, with generally similar performance present across a broad range of states and mixtures. The confusion matrix over the full data set conditions, including all call types with all overlapping and noisy calls, are shown in Table 1. The overall accuracy for the full set is 60.1%. The pattern shown by the confusion matrix indicates that a significant number of the errors are made between highly confusable call types and between basic call types and their corresponding call-combinations. By roughly ordering the columns according to confusability between calls, similarity can be observed by looking along the diagonal for blocks with significant error rates. Some of these patterns are highlighted in the confusion matrix for reference. Similarity between RRM (Roa-Rumble) and RUM (Rumble) and to a lesser extent ROR (Roa) is noted, as well as substantial similarity between SQS (Squeak-Squeal) and SQL (Squeal). These similarities are expected because combination calls are made up of the basic call types with which they are confused, and the error patterns help confirm the repertoire categories. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{1}{|c|}{} & \multicolumn{10}{|c|}{**Classification**} \\ \cline{3-14} \multicolumn{1}{|c|}{} & \multicolumn{1}{|c|}{_BRK_} & RRM & _ROR_ & _RUM_ & SKC & SQG & _SQK_ & _SQL_ & SQS & _TMP_ & TRR \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **RRK** \\ \end{tabular} } & \multirow{2}{*}{42} & 0 & 10 & 20 & 0 & 0 & 1 & 5 & 0 & 6 & 0 \\ \cline{1-1}  & RRM & 0 & 5 & 8 & 4 & 0 & 0 & 0 & 0 & 0 & 0 \\ \cline{1-1}  & _ROR_ & 21 & 5 & 134 & 32 & 0 & 0 & 0 & 11 & 2 & 8 & 0 \\ \cline{1-1}  & _RUM_ & 7 & 1 & 20 & 87 & 1 & 0 & 0 & 32 & 10 & 23 & 0 \\ \cline{1-1}  & SKC & 0 & 0 & 2 & 0 & 64 & 5 & 0 & 10 & 22 & 3 & 0 \\ \cline{1-1}  & SQG & 0 & 0 & 1 & 0 & 8 & 13 & 3 & 2 & 1 & 0 & 0 \\ \cline{1-1}  & _SQK_ & 11 & 0 & 0 & 5 & 26 & 2 & 253 & 16 & 11 & 7 & 0 \\ \cline{1-1}  & _SQL_ & 12 & 0 & 2 & 38 & 17 & 0 & 11 & 323 & 103 & 66 & 0 \\ \cline{1-1}  & _SQS_ & 0 & 0 & 0 & 7 & 26 & 4 & 13 & 75 & 75 & 1 & 0 \\ \cline{1-1}  & _TMP_ & 6 & 0 & 1 & 22 & 2 & 1 & 17 & 24 & 4 & 227 & 0 \\ \cline{1-1}  & TRR & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 4 \\ \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix for 12 state HMM with 3 mixtures. Basic call types are shown in bold. BRK=Bark, RRM=Roa-Rumble, ROR=Roa, RUM=Rumble, SKC=SqueakTrain, SQG=Squeak-Bark, SQK=Squeak, SQL=Squeal, SQS=Squeak-Squeal, TMP=Trumpet, TRR=Trumpet-Roa. Overall accuracy is 60.1% (_vs._ chance accuracy 28.0%). More accurate classification is possible by including only the basic call types, and by excluding calls with high noise degradation and calls that overlap with other calls or sporadic sounds. This improvement is shown in Table 2, where the accuracy for a 12 state HMM classifier with three Gaussian mixtures is 85.0%, across 186 clean vocalizations.