## 4 Results  
### Classification of Whale-Call Data  
The classification on the large-scale whale-call dataset consists of 3 sub-tasks, which aims to classify the data respectively into 2 species (pilot whales and killer whales), 4 groups (Norwegian killer whales, Iceland killer whales, Bahamas short-finned whales and Norwegian long-finned whales) and 16 pods, as illustrated in Table 1. The proposed approach is actually implemented on the classification into 16 pods, while the classifications into 2 species and 4 groups are then obtained on the basis of the results of the 16-pod classification. As shown in Table 2, the classification accuracies are achieved based on the validation dataset and compared with the corresponding results of other researchers [9]. It is shown that the classifications using log-mel features (ResN-logm and Xcep-logm) have higher accuracies than those using raw waveforms (ResN-wav and Xcep-wav), which probably indicates that the time-frequency transforms are more powerful for extracting discriminative features than the 1-D convolutional layers for classification. The time-frequency features also have a much clearer physical meaning. Moreover, the ResNext101 model is demonstrated to have better performance than Xception in classifying whale-call data. A significant improvement is observed in the comparison between the Wndchrm method and the proposed approaches. The results of models trained from scratch are also presented in Table 2 as a reference, where the accuracies decrease by about 2-4% compared with the results of the corresponding models pre-trained on ImageNet. In addition, as shown in Table 3, the classifiers have different performances for these 16 pods, where accuracies are obtained ranging from 100% to 63.2%. It should be noted that a class imbalance problem exists among the pods, whereby the largest pod has 1526 audio files and the smallest one only has 116, which might have an effect on the class-wise classification results. This problem will be considered in our further study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Input** & **Classifier** & **Classify to 2 Species** & **Classify to 4 Groups** & **Classification to 16 Pods** \\ \hline Wndchrm & spectrum & Polynomial decomposition \& Fisher scores & 92\% & \(\times\) & 44–62\% \\ \hline ResN-wav & wave & ResNet101 & 99.5\% & 97.7\% & 91.6\% \\ \hline ResN-logm & logmel & ResNet101 & **99.7\%** & **99.2\%** & **97.6\%** \\ \hline Xcep-wav & wave & Xception & 99.3\% & 97.3\% & 91.2\% \\ \hline Xcep-logm & logmel & Xception & 99.5\% & 98.3\% & 95.1\% \\ \hline ResN-wav \({}^{*}\) & wave & ResNet101 (no-pretrain) & 95.9\% & 94.3\% & 88.3\% \\ \hline ResN-logm \({}^{*}\) & logmel & ResNet101 (no-pretrain) & 97.2\% & 96.8\% & 94.2\% \\ \hline Xcep-wav \({}^{*}\) & wave & Xception (no-pretrain) & 95.3\% & 93.9\% & 87.9\% \\ \hline Xcep-logm \({}^{*}\) & logmel & Xception (no-pretrain) & 96.7\% & 95.6\% & 93.2\% \\ \hline \hline \end{tabular}  
* Trained from scratch.  
\end{table}
Table 2: Comparison of classification results.  
Figure 2: The evolution of accuracies and losses in the training based on the ResN-logm and ResN-wav models (color online).