## 5 Discussion  
Our experiments indicate that raw audio ConvNets (i.e., ConvNets classifying raw audio samples) can perform better than ConvNets that classify audio spectrogram images on some audio datasets. Specifically, on the BUZZ2 validation dataset, SpectConvNet outperformed the four ML models but performed considerably worse than all four raw audio ConvNets. While it may be possible to experiment with different image classification ConvNet architectures to improve the classification accuracy of SpectConvNet, RawConvNet's classification accuracies of 95.21% on the BUZZ1 validation dataset and 96.53% on the BUZZ2 validation dataset are quite adequate for practical purposes because, unlike SpectConvNet, it classifies unmodified raw audio signals without having to convert them into images. Consequently, it trains and classifies faster, and is less energy intensive, which makes it a better candidate for in situ audio classification on low voltage devices such as Raspberry Pi or Arduino. When we compared the performance of RawConvNet, a shallower ConvNet with a custom layer, with the three deeper ConvNets without custom layers on both the BUZZ1 and BUZZ2 validation datasets, we observed (see Table 23) that adding more layers may not necessarily result in improved classification performance. The summary results in Table 23 and the confusion matrices in Appendix B indicate that the deeper networks performed worse than RawConvNet on both validation datasets. RawConvNets also achieved lower validation losses. These results indicate that on BUZZ1, where the validation samples were separated from the training and testing samples by beehive and location, RawConvNet generalized better than the three deeper ConvNets and performed on par with logistic regression and random forests. While KNN and SVM OVR performed worse than RawConvNet, logistic regression, and random forests, both models (KNN and SVM OVR) outperformed the three deeper ConvNets. The picture is different on BUZZ2, where the validation samples were completely separated from the training samples by beehive, location, time, and bee race. In terms of total validation accuracies, all raw audio ConvNets generalized better than their ML counterparts and outperformed them. It it interesting to observe that, for the ML models, feature scaling did not contribute to performance improvement. While the total accuracies give us a reasonable description of model performance, it is instructive to take a look at how the models performed on both validation datasets by audio sample category. This information is summarized in Tables 24 and 25. On BUZZ1, both the DL and ML models generalized well in the bee category. In the cricket category, logistic regression, KNN, RawConvNet, and ConvNet 1 achieved a validation accuracy above 90%. In the noise category, the only classifiers with a validation accuracy above 90% were RawConvNet, logistic regression, and random forests. On BUZZ2, only RawConvNet achieved a validation accuracy above 90% in the bee category. Thus, in the bee category, this was the only ConvNet that generalized well to a validation dataset where the audio samples in the training dataset were separated from the validation dataset by beehive, location, time, and bee race. All classifiers with the exception of SVM OVR and random forests generalized well in the cricket category. In the noise category, RawConvNet was again the only classifier with a validation accuracy above 90%. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **BUZZ1** & **BUZZ2** \\ \hline RawConvNet (\(n\ =\ 80\)) & 95.21\% & 96.53\% \\ ConvNet 1 (\(n\ =\ 80\)) & 74.00\% & 82.96\% \\ ConvNet 2 (\(n\ =\ 80\)) & 78.08\% & 83.53\% \\ ConvNet 3 (\(n\ =\ 80\)) & 75.04\% & 83.40\% \\ Logistic regression & 94.60\% & 68.53\% \\ Random forests & 93.21\% & 65.80\% \\ KNN (\(n\ =\ 5\)) & 85.47\% & 37.42\% \\ SVM OVR & 83.91\% & 56.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Summary of validation accuracies on BUZZ1 and BUZZ2 validation datasets.