# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
## 2 Audio Datasets
The standard ML models were trained on feature vectors of 193 elements extracted with Librosa [15], an open source Python library for audio analysis: 40 Mel Frequency Cepstral Coefficients (MFCCs), 12 chroma coefficients, 128 mel-spectrogram coefficients, seven spectral contrast coefficients, and six tonnetz coefficients. On BUZZ1, a shallower ConvNet with a custom layer outperformed three deeper ConvNets and performed on par with the standard ML methods trained to classify feature vectors extracted from raw audio samples. On BUZZ2, a more challenging audio dataset, all ConvNets outperformed the four ML methods and a ConvNet trained to classify spectrogram images of audio samples. We observed that a major trade-off between deep learning (DL) and standard ML was between feature engineering and training time: while the ConvNets required no feature engineering and generalized better on raw audio files (i.e., amplitude vectors), they took considerably more time to train than the standard ML methods. To continue our investigation of audio beehive monitoring, we curated two more audio datasets (BUZZ3 and BUZZ4) in 2019 from the audio samples acquired by different BeePi monitors in Logan and North Logan, UT. BUZZ3 includes 15,254 audio samples manually labeled as B (5121 samples), C (5346 samples), and N (4787 samples). In 2020, we augmented BUZZ3 with a fourth category of lawn moving (L) into which we placed 3340 audio samples where two out of the three human listeners could hear a lawnower's sound. Thus, BUZZ3 is a proper subset of BUZZ4. The sample distribution of BUZZ3 and BUZZ4 are given in Table 3.  
We used BUZZ1, BUZZ2, and BUZZ3 to investigate whether automated feature engineering could improve standard ML methods to perform on par with DL methods [16]. We experimented with recursive feature elimination (RFE) [17], sequential feature selection (SFS) [18], relief-based feature selection [19], and RF feature selection [20] to find optimal feature subsets. Our feature space included thirty-four audio features (e.g., zero crossing rate, energy, spectral flux, MFCCs, etc.) extracted with the pyAudioAnalysis library [21]. We confined our investigation to LR, KNN, SVM, and RF and compared their performance with that of the top performing ConvNets we had previously trained on BUZZ1, BUZZ2, and BUZZ3 [8]. Table 4 shows the accuracies of our models on BUZZ1, BUZZ2, and BUZZ3 with the thirteen MFCCs selected by all automated feature engineering methods. On BUZZ1, the best validation accuracy of 98.43% was achieved by the RF with 100 decision trees; on BUZZ2, the best validation accuracy of 95.33% was achieved by LR; on BUZZ3, the best validation accuracy of 97.91% was achieved by RF. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bee (B)** & **Cricket (C)** & **Noise (N)** & **Lawn (L)** & **Total** \\ \hline Training & 2880 & 3600 & 2520 & 2120 & 11,120 \\ \hline Testing & 1071 & 577 & 1098 & 840 & 3586 \\ \hline Validation & 1170 & 1169 & 1169 & 380 & 3888 \\ \hline Total & 5121 & 5346 & 4787 & 3340 & 18,594 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Audio sample distribution in BUZZ3 (first three columns) and BUZZ4 (first four columns). We also compared the accuracies of the best performing models on each dataset with the accuracies of the best performing ConvNet that we had previously trained, tested, and validated on the same datasets [8]. Table 5 gives the model accuracies. The RF model slightly outperformed the ConvNet on BUZZ1 and BUZZ2 while the ConvNet slightly outperformed LR on BUZZ2. The validation accuracies of all models were above 95% on all datasets. A lesson we learned from this investigation is that standard ML models are a viable alternative to ConvNets on these datasets, because they train much faster than ConvNets and produce smaller RAM and disk memory footprints. In 2020, we continued our investigation of optimal feature sets for audio classification with standard ML models and experimented with RFE and SFS [22].