# Building Ensemble of Resnet for Dolphin Whistle Detection
## 4 Conclusions  
The surge in human activities in marine environments has led to an influx of boats and ships that emit powerful acoustic signals, often impacting areas larger than 20 square kilometers. The underwater noise from larger vessels can surpass 100 PSI, disturbing marine mammals' hearing, navigation, and foraging abilities, particularly for coastal dolphins [38; 39]. Therefore, the monitoring and preservation of marine ecosystems and wildlife is paramount. However, conventional monitoring technologies depend on detection methods that are less than ideal, thereby hindering our capacity to carry out extensive, long-term surveys. While automatic detection methods could significantly enhance our survey capabilities, their performance is typically subpar amidst high background noise levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **AUC** & **Precision** & **Recall** \\ \hline Pampguard [14] & 0.664 & — & 0.755 & 0.195 \\
[28] & 0.923 & 0.960 & 0.905 & 0.896 \\ ResNet50(10)\_DA & **0.949** & **0.973** & **0.965** & **0.902** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with the literature using four measures.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Here** & & [28] & **Pampguard [14]** & &
\begin{tabular}{c} **Cohen’s Kappa** \\ **[28]** \\ \end{tabular} & **Pampguard [14]** \\ \hline Noise & Noise & Whistle & Noise & Whistle & Noise & & & \\ Noise & 4124 & 88 & 3963 & 249 & 4044 & 168 & 0.8919 & 0.8383 & 0.1797 \\ Whistle & 260 & 2397 & 277 & 2380 & 2139 & 518 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrices and Cohen’s kappa coefficient.  
In this paper, we illustrated how deep learning techniques involving data augmentation can identify dolphin whistles with remarkable accuracy, positioning them as a promising candidate for standardizing the automatic processing of underwater acoustic signals. We obtained state-of-the-art results and provided a training and test set for fair comparison. In terms of accuracy, we obtained a nearly 0.03 accuracy gain. The MATLAB/PyTorch source code used in this study is freely provided ([[https://github.com/LorisNanni/](https://github.com/LorisNanni/)]([https://github.com/LorisNanni/](https://github.com/LorisNanni/)) accessed on 7 July 2023).  
Despite the need for additional research to confirm the efficacy of such techniques across various marine environments and animal species, we are confident that deep learning will pave the way for developing and deploying economically feasible monitoring platforms. We hope that our new baseline will further the comparison of future deep learning techniques in this area.  
Finally, we should stress the main cons of using this dataset as a benchmark: the training and test set were from the same region (Dolphin's Reef in Eilat, Israel), and the samples were collected using the same acoustic recorder.  
Conceptualization, L.N.; methodology, L.N. and D.C.; software, L.N. and D.C.; writing--original draft preparation, L.N. and S.B.; writing--review and editing, L.N., D.C. and S.B.; supervision, L.N. and S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[[https://csms-acoustic.haifa.ac.il/index.php/s/2](https://csms-acoustic.haifa.ac.il/index.php/s/2) [PERSON]]([https://csms-acoustic.haifa.ac.il/index.php/s/2](https://csms-acoustic.haifa.ac.il/index.php/s/2) [PERSON]) accessed on 7 July 2023.  
The authors would like to acknowledge the support that NVIDIA provided through the GPU Grant Program. The authors also used a donated TitanX GPU to train the deep networks used in this work.  
The authors declare no conflict of interest.