## 6 Conclusions  
Fish acoustic communication capabilities are well-known from literature; however, their species-specific characteristics have been little studied for automatic identification purposes. In this paper, we have introduced a novel automatic classification method of fish species based on their bioacoustic emissions, which allows the analysis of remote underwater data without human intervention. The acoustic signals have been parameterized through a combination of time-variable and frequency domain information, obtaining a complete representation of the signals. The results of this approach are promising with an average classification accuracy of 95.58% and a standard deviation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Database** & **Features** & **Classification** & **Accuracy** \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{2 species} & Drumming frequency, & \multirow{3}{*}{Threshold} & \multirow{3}{*}{80\% and 90\%} \\  & & pitch, short & & \\  & & and long term partial loudness & & \\  & & Morphology, spatial location, & & \\
[18] & 3 species & acoustic energy & SVM 1 [FOOT:1]Footnote 1: SVM: Support Vector Machine; BGOT: Balanced-Guaranteed Optimized Tree; MFCC: Mel Frequency Cepstral Coefficients; LFCC: Linear Frequency Cepstral Coefficients; SE: Shannon Entropy; SL: Syllable Length.[ENDFOOTNOTE] & 89.55\% \\  & & and bathymetry & & \\
[36] & 2 species & Morphology & SVM & 78.59\% \\
[19] & 10 species & Color, texture & & \\  & & and morphology & & \\  & & Euclidean network technique, & & \\
[37] & 15 species & quadratic network technique & & \\  & & and triangulation technique & & \\ \hline \hline This work & Fish dataset (102 species) & MFCC/LFCC/SE/SL 1 & SVM & 95.58\% \(\pm\) 8.59\% \\ \hline \hline \end{tabular}  
* SVM: Support Vector Machine; BGOT: Balanced-Guaranteed Optimized Tree; MFCC: Mel Frequency Cepstral Coefficients; LFCC: Linear Frequency Cepstral Coefficients; SE: Shannon Entropy; SL: Syllable Length.  
\end{table} 
Table 4: Comparison of the proposed system vs. state-of-the-art. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy Median (\%) \(\pm\) Std.** & **Precision** & **Recall** & **F-Measure** \\ \hline “Unnatural sounds” & 95.21\% \(\pm\) 8.26\% & 0.9452 & 0.9520 & 0.9486 \\ “Natural sounds” & 98.17\% \(\pm\) 6.48\% & 0.9839 & 0.9817 & 0.9828 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classifier performance by sound type. of 8.59%. This shows that the proposed system achieves a better recognition rate than other methods based on computer vision techniques. Hence, these results suggest that this technique should be studied as an alternative method for estimation of fishery resources or to map species biodiversity. It has been verified over a dataset formed of 102 fish species with a relatively low number of samples. Unfortunately there are few public datasets of fish sounds to perform a broad study. In fact, the authors are not aware of other studies that have automatically and acoustically classified such a large number of fish species. Furthermore, the system has been tested in small training set scenarios, proving the robustness of the method. Even using only 5% of the samples for training, the system was able to achieve results above 80%. In this research, it has also been found that MFCCs are more efficient for modeling these signals due to the sounds produced by fish mainly being emitted at low frequencies. However, adding information of high frequencies through LFCCs has significantly improved the performance on the test set. Finally, temporal information has been incorporated into the model, Shannon Entropy and Syllable Length, achieving a stronger taxonomic classification system due to the clear temporal differences among their acoustic signals.