# Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion
## 6 Results and Discussions  
A set of experiments carried out using different features, LFCC and MFCC, and different classifiers, RF and SVM, are presented in this section. The experimental results are presented in Table 1, where the success rate is shown in terms of mean and standard deviation (std), and the accuracy in each experiment has been computed dividing the number of sounds correctly identified by the total number of sounds. The columns, TT and TST represent the mean training time (TT) and testing time (TST) in seconds. As can be observed, with respect to the performance of the classification algorithm, both algorithms have reached a high success rate. A high proportion of the produced sounds by insects are of very short time duration and great amplitude, so they generate a clear spectrogram which allows an optimal segmentation. Therefore, the input data to the classification phase present very little noise, and thus, the discrimination among classes and the success rate is improved regardless of the classification algorithm. On the other hand, analyzing the impact of the type of acoustic parameterization, it can be stated that the fusion of LFCC and MFCC features gives higher accuracy than each isolated one. LFCC exceeds to MFCC in each experiment because insects emit signals predominantly at high frequencies and with high energy levels that can reach up to 100 dB. Hence, the best representation of high frequencies by the LFCC provides a major amount of information to the system. Once all the results are obtained, it can be demonstrated that insects' sounds classification system presented in this paper can identify the individuals in the database, offering a better success rate than the ones shown in the state of the art, and showing an improvement in the field. Figure 3 shows a swarm plot with the results of the confusion matrix for each dataset utilizing the best approach in the experiments (SVM + MFCC/LFCC fusion). As can be noticed, the high concentration of classes is around 100% success rate. Besides, one of the insects could hardly be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Features** & **Classifier** & **TT (sec)** & **TST (sec)** & **Accuracy (\%) \(\pm\) std** \\ \hline \multirow{5}{*}{SINA (255 species)} & \multirow{2}{*}{MFCC} & RF & 51.63 & 0.37 & 91.02\% \(\pm\) 17.66 \\  & & SVM & 76.18 & 63.43 & 95.20\% \(\pm\) 10.59 \\ \cline{2-6}  & & RF & 49.34 & 0.35 & 94.18\% \(\pm\) 12.81 \\ \cline{2-6}  & & SVM & 62.93 & 77.80 & 96.97\% \(\pm\) 7.13 \\ \cline{2-6}  & & RF & 79.35 & 0.40 & 94.78\% \(\pm\) 12.72 \\ \cline{2-6}  & & SVM & 93.00 & 66.55 & **97.99\% \(\pm\) 6.61** \\ \hline \multirow{5}{*}{Insectsingers (88 species)} & \multirow{2}{*}{MFCC} & RF & 6.65 & 0.08 & 94.71\% \(\pm\) 11.66 \\  & & SVM & 2.31 & 1.41 & 98.23\% \(\pm\) 5.40 \\ \cline{1-1} \cline{2-6}  & & RF & 6.45 & 0.08 & 95.44\% \(\pm\) 10.82 \\ \cline{1-1} \cline{2-6}  & & SVM & 2.36 & 1.43 & 99.15\% \(\pm\) 2.62 \\ \cline{1-1} \cline{2-6}  & & RF & 9.66 & 0.09 & 96.72\% \(\pm\) 8.34 \\ \cline{1-1} \cline{2-6}  & & SVM & 3.57 & 1.61 & **99.40\% \(\pm\) 2.07** \\ \hline \multirow{2}{*}{Full insects dataset (363 species)} & MFCC & & 146.98 & 80.22 & 94.83\% \(\pm\) 14.22 \\ \cline{1-1}  & LFCC & SVM & 155.40 & 77.65 & 96.94\% \(\pm\) 8.29 \\ \cline{1-1} \cline{2-6}  & MFCC + LFCC & & 246.39 & 170.29 & **98.07\% \(\pm\) 6.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification results. identified in all iterations. Specifically, for the database SINA the grasshopper _Stilpnochlora couloniana_ only was identified 30% of the time (40% in the full dataset) due to a bad segmentation of the calls. Similarly, the audio file of the katydid _Scudderia furcata_ contained only 2 calls that were divided into 4 by the segmentation algorithm which meant that only 65% of the time could be recognized. For the Insectsingers database, all results per class remain above 86%. The lowest result was 86.4% belonging to the cicada _Dicerprocta marevagans_.