## 5 Experimental Procedure
### Results and Discussions  
The experiments consisted of two phases. The first phase performed an evaluation of different algorithms and features in order to find the best model capable of recognizing fish calls. For that reason, 50% of the feature vector samples have been taken for training purposes and the rest for testing. In the second stage, the training samples have been reduced to 5% in order to verify the robustness of the methodology. Table 1 shows how the proposed combination of features reinforces the learning procedure as a consequence of fusing the temporal and frequency information, regardless of the algorithm used. MFCCs clearly outperform LFCCs when used individually because fish acoustic signals are basically generated in low frequencies and MFCCs coefficients emphasize that region of the spectrum. Meanwhile, LFCCs represent higher frequencies better so they perform poorly in all experiments. In fact, three species could not be identified at all: _Haemulon flavolineatum_, _Merlucius bilinearis_ and _Diplectrum formosum_. To parametrize lower as well as higher frequencies, both features were fused (MFCC + LFCC). The experiments verified that the fusion of information increases the performance of the system enhancing the recognition rate. Finally, the temporal information has been added, SE and SL, achieving an evident improvement in accuracy. It simplifies the work of the classifier due to significant temporal differences among the fish calls. Therefore, it confirms that the proposed system is effective for identifying and classifying fish acoustic signals with a high success rate. As expected, KNN requires the lowest training times due to its simplicity. Even so, KNN outperforms other algorithms in the first two scenarios, applying MFCCs and LFCCs independently. In this case, SVM and RF are not able to extract discriminant information to separate the classes properly. After grouping the features, SVM slightly surpasses KNN and overcomes RF. The reason lies in the fact that SVM with the Gaussian kernel performs better than KNN with nonlinear data. Furthermore, RF needs a larger set of training data to define a robust model. Finally, the SVM approach \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Features** & **Classification** & **Training time (s)** & **Test time (s)** & **Accuracy Median \% \(\pm\) Std.** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Fish dataset (102 species) \\ \end{tabular} } & 1 & KNN 1 & 0.04 & 0.01 & 91.94\% \(\pm\) 15.27\% \\  & MFCC & RF 1 & 0.76 & 0.01 & 90.34\% \(\pm\) 15.01\% \\  & SVM 1 & 0.28 & 0.09 & 91.20\% \(\pm\) 18.25\% \\ \cline{2-6}  & 1 & KNN & 0.04 & 0.01 & 89.05\% \(\pm\) 16.04 \\ \cline{2-6}  & LFCC & RF & 0.76 & 0.01 & 87.41\% \(\pm\) 16.46\% \\  & & SVM & 0.25 & 0.09 & 81.60\% \(\pm\) 28.14\% \\ \cline{2-6}  & & KNN & 0.05 & 0.02 & 94.27\% \(\pm\) 11.10\% \\  & MFCC + LFCC & RF & 1.29 & 0.02 & 93.63\% \(\pm\) 10.59\% \\  & SVM & 0.31 & 0.10 & 94.72\% \(\pm\) 12.13\% \\ \cline{2-6}  & & KNN & 0.05 & 0.02 & 95.24\% \(\pm\) 8.59\% \\ \cline{2-6}  & MFCC + LFCC & RF & 1.14 & 0.01 & 93.56\% \(\pm\) 11.42\% \\  & + SE 1 + SL.1 & SVM & 0.34 & 0.10 & **95.58\% \(\pm\) 8.59\%** \\ \hline \hline \end{tabular}  
* MFCC: Mel Frequency Cepstral Coefficients; KNN: K-Nearest Neighbors; RF: Random Forest; SVM: Support Vector Machine; LFCC: Linear Frequency Cepstral Coefficients; SE: Shannon Entropy; SL: Syllable Length.  
\end{table} 
Table 1: Classification results. based on temporal-frequency characteristics achieves the best taxonomic classification rate of 95.58%. Hence, it was selected for the rest of the experiments. Figure 4 presents the average accuracy results for each class where the best approach was applied (MFCC-LFCC-SE-SL with SVM). It reveals poor classification rates in two classes that cannot be identified properly, and only six species obtained an accuracy lower than 80%.