# Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations
## 3 Results
### Pig Anomaly Classification Results
As previously described in Section 2.2.2, to apply the 8-bit filter clustering method to all layers of MnasNet, the MBConv layers composed of \(5\times 5\) filters were changed to comprise two hierarchical \(3\times 3\) filters. Therefore, the interior of the existing MBConv structure was changed to a structure with two DWConvs, named MBConv2, as shown in Figure 7d. In addition, in MBConv in the MnasNet model, layers having the same number of filters were repeated; hence, we represented them as one and added symbols \(\times 2/\times 3/\times 4\) on the right side of the layers (see Figure 3a). However, when the 8-bit filter clustering algorithm is applied to MnasNet, the number of filters belonging to each layer of the repeated MBConv changes. Therefore, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Predicted**} \\ \cline{2-6}  & **Healthy Cough** & **Gront** & **MH** & **PMWS** & **PRRS** & **Scream** \\ \hline Healthy Cough & **179** & 1 & 0 & 0 & 0 \\ Grunt & 5 & **179** & 0 & 6 & 0 & 2 \\ MH & 0 & 4 & **121** & 1 & 0 & 0 \\ PMWS & 3 & 9 & 1 & **239** & 1 & 0 \\ PRRS & 5 & 3 & 0 & 0 & **259** & 2 \\ Scream & 12 & 3 & 0 & 0 & 0 & **237** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for identification of pig abnormalities (Test: Clean + All synthesized noise dataset). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Noise Condition**} & \multicolumn{5}{c}{**F1-Score**} \\ \cline{2-6}  & **MnasNet** & **MnasNet** & **MnasNet** & **Filter** & **Proposed Method** & **Proposed Method** \\  & **DM 1.0** & **DM 0.75** & **DM 0.5** & **Clustering**[29] & **Only 3 × 3 Layers** & **All Layers** \\ \hline Clean & 0.985 & 0.960 & 0.913 & 0.991 & 0.976 & **0.993** \\ \hline SNR 20 & 0.935 & 0.935 & 0.893 & **0.991** & 0.970 & 0.987 \\ SNR 15 & 0.981 & 0.923 & 0.910 & 0.973 & 0.976 & **0.978** \\ SNR 10 & 0.954 & 0.906 & 0.830 & 0.991 & 0.985 & **0.993** \\ SNR 5 & 0.963 & 0.891 & 0.855 & 0.973 & 0.973 & **0.993** \\ SNR 0 & 0.931 & 0.863 & 0.773 & 0.983 & 0.986 & **0.987** \\ \hline Radio operation & **0.876** & 0.790 & 0.870 & 0.863 & 0.796 & 0.820 \\ Weak footsteps & 0.871 & 0.820 & 0.835 & 0.833 & 0.846 & **0.917** \\ Strong footsteps & 0.916 & 0.860 & 0.768 & 0.906 & **0.958** & **0.958** \\ Door opening & 0.783 & 0.721 & 0.710 & 0.735 & **0.861** & 0.845 \\ \hline Average & 0.922 & 0.867 & 0.835 & 0.924 & 0.933 & **0.947** \\ \hline Standard deviation & 0.062 & 0.072 & 0.067 & 0.088 & 0.070 & 0.065 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sound-based pig abnormality identification results (Train: clean + SNR 0 synthesized dataset). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MnasNet** & **MnasNet** & **MnasNet** & **Filter** & **Proposed Method** & **Proposed Method** \\  & **DM 1.0** & **DM 0.75** & **DM 0.5** & **Clustering**[29] & **Only 3 × 3 Layers** \\ \hline
**No. of Model** & 2,727,310 & 1,607,106 & 749,118 & 2,268,713 & 2,094,722 & **646,624** \\
**Parameters** & 3,729 & 3,729 & 3,729 & 3,729 & 0.412 s/image & 0.378 s/image & **0.253** s/image \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the number of parameters of the pig-anomaly detector model and the execution time of the model on the TX-2 board (Train: clean + SNR 0 synthesized dataset). the MBConv, which had the same number of filters that was repeated 3 times in the basic MnasNet structure, was drawn only once and marked with the symbol \(\times 3\) on its right side (see 1 in Figure 3a). The same MBConv had to be expressed by dividing it into 3 MBConvs with different numbers of filters after applying our proposed method (see 1 in Figure 7a). Tables 7 and 8 summarize a comparison of results between other studies and our proposed method. However, because the previous studies conducted experiments without considering the noise generated in pigpens, the results using only clean data in the test dataset were used to ensure a fair and accurate comparison.