# Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion
## 7 Conclusions and Future Work  
This work has introduced a classification system through a data fusion of Mel and lineal cepstral coefficients of chants of cricket, katydid, and cicada species, which accomplished the detection and identification of these sounds. These insect species are especially suitable for acoustic recognition because they tend to produce loud pure-tone songs. The results obtained have demonstrated that the fusion of these cepstral parameters improves the representation of the insect sound signals adding information of the high and low part of the spectrum enhancing the classification results. In addition, the increase in the number of features used by data fusion leads to a slight increment in training and classification times. In the experiments conducted, the SVM algorithm using a Gaussian kernel has achieved better results than RF due to its major ability to separate nonlinear relationships in the data. Nevertheless, the differences are less than 5% because of the clarity of the sounds emitted by these insect species.  
The robustness of the system has been tested by classifying multiple species of insects simultaneously; 343 species, with a high success rate of 98.07%. The proposed method achieves superior results compared to previous methods [14; 15; 16; 17], due to the proximity to a 100% classification. Moreover, this approach can be applied to other taxonomic groups adjusting the system parameters to the particularities of their sound emissions.  
More work should be done in the field of bioinformatics for automatic species classification to develop more refined methods. In particular, future research should be oriented to successfully detect, recognize and classify insects (katydids, crickets and cicadas) by the family, suborder, genus and species level with a higher success rate than the ones presented in the state-of-the-art or in this study.  
Conceptualization, J.J.N., C.M.T.-G. and D.S.-R.; methodology, J.J.N., C.M.T.-G. and D.S.-R.; software, J.J.N.; validation, J.J.N.; investigation, J.J.N., C.M.T.-G., D.S.-R. and J.B.A.-H.; resources, J.J.N., C.M.T.-G. and D.S.-R; writing--original draft preparation, J.J.N., C.M.T.-G., D.S.-R and J.B.A.-H.; writing--review and editing, J.J.N., C.M.T.-G., D.S.-R. and J.B.A.-H.; supervision, C.M.T.-G. and D.S.-R.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Features** & **Classifiers** & **Results** \\ \hline
[14] & SINA (313 sounds) & LFCC and length frame & PNN, GMM and HMM & 86.3\% \\ \hline
[29] & Insect Sound Library: & & & \\  & United States & & & \\  & Department of & MFCC & PNN & 96.17\% \\  & Agriculture (USDA) & & & \\  & (50 species) & & & \\ \hline
[15] & Insect Sound Library: & & & \\  & United States & & & \\  & Department of & & Cepstral & HMM & 90.98\% \\  & Agriculture (USDA) & & & \\  & (50 species) & & & \\ \hline
[30] & 26 grasshopper species & MFCC & HMM & 99.31\% \\ \hline
[16] & British Orthoptera & Time Domain Signal & Neural & 99\% \\  & (25 katydids) & Coding & Network & 99\% \\ \hline This work & Insect Dataset (343 species) & MFCC/LFCC fusion & SVM & 98.05\% \(\pm\) 6.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposal vs. the state-of-the-art.