## 5 Discussion
The main trade-off between the ConvNets and the ML methods was between feature engineering and training time. Our scientific diary indicates that it took us approximately 80 h of research and experimentation to obtain the final set of features for the four ML methods that gave us an optimal classification performance. On the other hand, once the feature engineering was completed, it took less than 5 min for each of the four ML methods to train both on BUZZ1 and BUZZ2. Specifically, on a personal computer with an Intel Core \(i\)7-4/[EMAIL_ADDRESS] GHz \(\times\) 8 processor with 15.5 GiB of RAM running 64-bit Ubuntu 14.04 LTS, RawConvNet took 12.83 h to complete 100 epochs of training on BUZZ1. The deeper ConvNets required more time for training. On the same computer and the same dataset, the three deeper ConvNets (i.e., ConvNet 1, ConvNet 2, ConvNet 3) and SpectConvNet took 15.14 h, 14.78 h, 16.94 h, and 20 h, respectively. In addition, to train SpectConvNet, we had to convert all audio samples into 2D spectrogram images. Thus, it took us a total of 79.69 h to train the ConvNets on BUZZ1 and 80 h to complete the feature engineering for the standard ML methods. Training the ConvNets on BUZZ2 took another 80 h of physical time, whereas the feature engineering for the ML methods on BUZZ2 required no physical time and took about 5 min of training once the feature vectors were extracted. RawConvNet took, on average, 462 s per epoch for training, whereas ConvNets 1, 2, 3 and SpectConvNet took, on average, 545 s, 532 s, 610 s, and 615 s, respectively. We did not evaluate our methods on ESC-50 [26], ESC-10 [26], and UrbanSound8K [57], three datasets used in some audio classification investigations. ESC-50 is a collection of 2000 short environmental recordings divided into five major audio event classes: animals, natural and water sounds, human non-speech, interior and domestic sounds, and exterior and urban noises. ESC-10 is a subset of ESC-50 of 400 recordings divided into 10 audio classes: dog bark, rain, sea waves, baby cry, clock tick, sneeze, helicopter, chainsaw, rooster, fire cracker. UrbanSound8K is a collection of 8732 short recordings of 10 audio classes of urban noise: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, jackhammer, siren, and street music. A principal reason why we did not consider these datasets in our investigation is that our objective is to provide a set of practical audio classification tools for electronic beehive monitoring. Consequently, it is important for us to train and test our models on audio samples captured by deployed electronic beehive monitors. Such audio samples are absent from ESC-50, ESC-10, and UrbanSound8K. Another, more fundamental, reason why we chose to curate and use our own domain-specific data is Wolpert \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method vs. Category** & **Bee** & **Cricket** & **Noise** \\ \hline RawConvNet (\(n\ =\ 80\)) & 100\% & 90.04\% & 97.71\% \\ ConvNet 1 (\(n\ =\ 80\)) & 99\% & 90.08\% & 28.57\% \\ ConvNet 2 (\(n\ =\ 80\)) & 99\% & 87.8\% & 46.28\% \\ ConvNet 3 (\(n\ =\ 80\)) & 100\% & 88\% & 35.14\% \\ Logistic regression & 99.66\% & 96\% & 95.42\% \\ KNN (\(n\ =\ 5\)) & 100\% & 93.8\% & 61.14\% \\ SVM OVR & 100\% & 73.8\% & 84.57\% \\ Random forests & 100\% & 87.6\% & 95.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Classification accuracy summary on BUZZ1 validation dataset by category. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method vs. Category** & **Bee** & **Cricket** & **Noise** \\ \hline RawConvNet (\(n\ =\ 80\)) & 96.90\% & 94.80\% & 99.40\% \\ ConvNet 1 (\(n\ =\ 80\)) & 86.50\% & 95.70\% & 66.70\% \\ ConvNet 2 (\(n\ =\ 80\)) & 76.60\% & 96.40\% & 77.60\% \\ ConvNet 3 (\(n\ =\ 80\)) & 86.40\% & 96.40\% & 67.40\% \\ Logistic regression & 51.50\% & 94.70\% & 59.40\% \\ KNN (\(n\ =\ 5\)) & 19.40\% & 90.80\% & 87.20\% \\ SVM OVR & 51.80\% & 11.10\% & 49.40\% \\ Random forests & 29.20\% & 48.80\% & 79.80\% \\ \hline \hline \end{tabular}
\end{table}