## 6 Conclusions  
We designed several convolutional neural networks and compared their performance with logistic regression, k-nearest neighbors, support vector machines, and random forests in classifying audio samples from microphones deployed above the landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio convolutional neural network with a custom layer outperformed three deeper raw audio convolutional neural networks without custom layers and performed on par with the four machine learning methods trained to classify feature vectors extracted from raw audio samples. On a more challenging dataset of 12,914 audio samples where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four machine learning methods and a convolutional neural network trained to classify spectrogram images of audio samples. Our investigation gives an affirmative answer to the question of whether ConvNets can be used in real electronic beehive monitors that use low voltage devices such as Raspberry Pi or Arduino. In particular, we demonstrated that a trained raw audio convolutional neural network can successfully operate in situ on a low voltage Raspberry Pi computer. Thus, our experiments suggest that convolutional neural networks can be added to a repertoire of in situ audio classification algorithms for audio beehive monitoring. The main trade-off between deep learning and standard machine learning was between feature engineering and training time: while the convolutional neural networks required no feature engineering and generalized better on the second, more challenging, dataset, they took considerably more time to train than the machine learning methods. To the electronic beehive monitoring, audio processing, bioacoustics, ecoacoustics, and agricultural technology communities, we contribute two manually labeled datasets (BUZZ1 and BUZZ2) of audio samples of bee buzzing, cricket chirping, and ambient noise and our data collection and audio classification source code. We hope that our datasets will provide performance benchmarks to all interested researchers, practitioners, and citizen scientists and, along with our source code, ensure the reproducibility of the findings reported in this article. The following materials are available online at [[http://www.mdpi.com/2076-3417/8/9/1573/s1](http://www.mdpi.com/2076-3417/8/9/1573/s1)]([http://www.mdpi.com/2076-3417/8/9/1573/s1](http://www.mdpi.com/2076-3417/8/9/1573/s1)). Figure S1. Training and validation losses of RawConvNet with receptive field size \(n~{}=~{}3\); Figure S2. Training and validation losses of RawConvNet with receptive field \(n~{}=~{}30\); Figure S3. Training and validation losses of RawConvNet with receptive field \(n~{}=~{}80\); Figure S5. Training and validation losses of RawConvNet with receptive field \(n~{}=~{}100\). Figure S6. Histograms of the gradients for layers 4 and 5 in ConvNet 1. The histograms on the left show the gradient distribution in the FC softmax layer in layer 4; the histograms on the right show the gradient distribution for the FC softmax layer in layer 5; Figure S7. Histograms of the gradients for layers 5 and 6 in ConvNet 2. The histograms on the left show the distribution of gradients for the FC softmax layer in layer 5; the histograms on the right show the gradient distribution for the FC softmax layer in layer 6; Figure S8. Histograms of the gradients for layers 6 and 7 in ConvNet 3. The histograms on the left show the distribution of the gradients for the FC softmax layer in layer 6; the histograms on the right show the gradient distribution for the FC softmax layer in layer 7; Figure S9. Histograms of the gradients in the FC softmax layer in layer 5 in RawConvNet; Figure S10.