## 3 Experiments
### Model Training  
To evaluate the performance of the proposed approach, the dataset is divided into a development dataset and a validation dataset. Since there are different numbers of audio files in the pods, we first divide all the audio in each pod equally into 5 groups and select 1 group from each pod to sum them up to make the validation dataset (1862 files in total). All the remaining audio in each pod is gathered into the development dataset (7457 files in total). A 4-fold cross-validation strategy [35] is performed on the development dataset to fine-tune the CNN models. This means that each time, 5960 samples are selected from the development dataset (the same proportion from all pods) to train the CNN models, while 1497 samples are used for testing.  
In the network training, the momentum stochastic gradient descent algorithm is used as the optimizer to solve the cross-entropy loss objective function. The learning rate is set as multi-steps, i.e., 0.01 for the first 35 epochs and multiplied by 0.1 for every next 20 epochs. The model is trained for 60 epochs in total before stopping, which ensures convergence. A batch size of 32 is applied, while rectified linear units (ReLUs) are used as nonlinear activation functions. The models are implemented by PyTorch using GPU acceleration on a hardware resource consisting of Xeon E5 2683V3 CPU and 2 GTX 1080 Ti GPU cards driven by CUDA with cuDNN. The classification accuracy of the validation audio clips, instead of CNN input segments, is employed to evaluate the performance of the proposed method. The clip-level accuracy is obtained from the segment-level accuracy through the majority-voting strategy. As shown in Figure 2, the evolution of accuracies and losses in the one-fold training based on the pre-trained ResNext101 model with both log-mel and wave inputs is illustrated. It can be observed that the model with log-mel input generally has lower losses and higher accuracies than the model with wave input. In addition, the training accuracies are shown to be significantly low due to the fact that the Mix-up technique has changed the labels of the training data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Location** & **Pods** & **Latitude** & **Longitude** & **Number of Samples** & **Sampling Rate** \\ \hline \multirow{4}{*}{Short-finned pilot whales} & & 1 & 24 & \(-\)77 & 1526 & 22,050 \\  & & 2 & 24 & \(-\)77 & 303 & 22,050 \\  & & 3 & 24 & \(-\)77 & 1148 & 22,050 \\  & & 4 & 24 & \(-\)77 & 329 & 22,050 \\ \hline \multirow{4}{*}{Killer whales} & & 6 & 63 & \(-\)20 & 215 & 32,000 \\  & Iceland & 7 & 63 & \(-\)20 & 116 & 48,000 \\  & & 22 & 63 & \(-\)20 & 976 & 32,000 \\ \hline \multirow{4}{*}{Killer whales} & & 8 & 68 & 16 & 823 & 32,000 \\  & & 9 & 68 & 16 & 598 & 32,000 \\  & & 10 & 68 & 16 & 288 & 32,000 \\  & & 12 & 68 & 16 & 610 & 32,000 \\  & & 13 & 68 & 16 & 357 & 32,000 \\  & & 24 & 68 & 16 & 200 & 32,000 \\ \hline \multirow{4}{*}{Long-finned pilot whales} & & 15 & 67 & 13 & 447 & 48,000 \\  & & 17 & 68 & 15 & 800 & 48,000 \\ \cline{1-1}  & & 19 & 67 & 14 & 559 & 48,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of recordings of whale calls.