## 5 Experimental Procedure
### Results and Discussions
The lowest identification rate was 46% for _Morone saxatilis_, whose sounds are constantly mistaken with _Pomadasys corvinaeformis_ and _Condoon noblis_, due to similar frequency responses. The second lowest classification is for _Trachinotus goodei_ with an accuracy of 68%. This recording presented high background noise and only four segmented syllables during the identification phase. However, 58 species reached 100% success, as a consequence of their distinct spectral characteristics. The following experiment verified that this approach is able to deal with a low number of training samples that were progressively reduced from 50% to 5%. In Table 2, the evaluation shows that the system can operate reasonably well under these circumstances. Only when the number of samples drops to 10% does the system show clear signs of decline in effectiveness. At this level, most species are only represented by one or two samples, thus SVM has serious difficulties in finding an optimal separation boundary among classes. Nevertheless, this approach is able to maintain the results of classification above 80%. Hence, it confirms the robustness of the technique. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Data (\%)** & **Accuracy Median (\%) \(\pm\) Std.** & **Precision** & **Recall** & **F-Measure** \\ \hline
5 & 81.77\% \(\pm\) 21.08\% & 0.8114 & 0.8168 & 0.8141 \\
10 & 83.27\% \(\pm\) 19.17\% & 0.8385 & 0.8318 & 0.8351 \\
20 & 88.15\% \(\pm\) 15.18\% & 0.8826 & 0.8807 & 0.8817 \\
30 & 92.57\% \(\pm\) 13.92\% & 0.9191 & 0.9244 & 0.9218 \\
40 & 94.99\% \(\pm\) 7.99\% & 0.9426 & 0.9490 & 0.9458 \\
50 & 95.58\% \(\pm\) 8.59\% & 0.9535 & 0.9558 & 0.9546 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classifier performance by training set size. Figure 4: Results for MFCC/LFCC/SE/SL + SVM. MFCC: Mel Frequency Cepstral Coefficients; LFCC: Linear Frequency Cepstral Coefficients; SE: Shannon Entropy; SL: Syllable Length; SVM: Support Vector Machine. Finally, an experiment has been performed applying our best approach (data fusion + SVM) splitting the database into two subsets: sounds obtained under duress (manual or electrically stimulated) or artificial conditions (\"unnatural sounds\") and sounds generated spontaneously or under natural conditions (\"natural sounds\"). Approximately 55.91% of the sounds are considered \"unnatural\" and the rest, 44.09%, as \"natural\". Performance results are shown in Table 3.  
Regardless of the nature of the sound, the proposed system is able to perform an efficient classification. The results are better than in previous experiments due to the reduction of the complexity of the problem. Furthermore, the \"natural sounds\" dataset achieved a higher result than the \"unnatural sounds\" dataset as a consequence of reverberant sounds with high frequency harmonics that are produced under artificial conditions. As mentioned previously, the number of studies concerning fish acoustic signals through intelligent systems is very limited. Therefore, a comparative study has been done with other state-of-the-art fish recognition techniques based on sonar images and morphological features. Table 4 draws attention to the discriminating capabilities of acoustic fish signals regarding visual methods. The proposed system was able to recognize a larger dataset of fish species with a higher classification rate than other techniques.