# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Settings
\begin{table}
\begin{tabular}{l l} \hline \hline
**Layer** & **Output Size** \\ \hline Input & 40 \(\times\) 501\(\times\) 1 \\ Conv(7 \(\times\) 1, 64) + BN + ReLU & 40 \(\times\) 501 \(\times\) 64 \\ Max pooling(4 \(\times\) 1) + Dropout(0.2) & 10 \(\times\) 501 \(\times\) 64 \\ Conv(10 \(\times\) 1, 128) + BN + ReLU & 1 \(\times\) 501 \(\times\) 128 \\ Conv(1 \(\times\) 7, 256) + BN + ReLU & 1 \(\times\) 501 \(\times\) 256 \\ Global max pooling + Dropout(0.5) & 256 \\ Dense & 128 \\ Softmax output & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall model architecture [13]. Figure 8: The input signal is first transformed from its (**a**) time-domain waveform to a (**b**) frequency-domain mel-spectrogram, which is then used in the baseline system. The proposed approach in (**c**) uses both the mel-spectrogram and augmented features. For the mix&shuffle, two data samples with the same label were randomly selected. Each data sample was sliced into four chunks, and the positions of these chunks were shuffled. Then, the chunks were randomly mixed. For the specaugment, the number of time masks was fixed at 1, with a time masking size ranging from 5 to 10 frames. The number of frequency masks was randomly selected between 1 and 2, with sizes ranging from 10 to 15 in the mel bin direction. The mixup parameter \(\alpha\) was set to 0.2. We used the F1-Score [28] as a metric which is commonly employed in multi-class evaluations with imbalanced data and was also used as the evaluation metric in DCASE 2018 Task5 [6]. This can be obtained by calculating the Precision and Recall of a class and then taking the harmonic mean. Precision, also referred to as specificity or Positive Predictive Value (PPV), is the ratio of true positives to predicted positives and can be calculated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
where TP represents true positives, which are correctly classified as positive, and FP represents false positives, which are incorrectly classified as positive when their true label is negative. Recall, also known as sensitivity or True Positive Rate (TPR), is the ratio of correctly predicted positives to actual positives and calculated as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
The F1-Score can be obtained by harmonically averaging the Precision and Recall, as follows:  
\[\text{F1-Score}=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{15}\]  
In multi-class classification tasks, in-depth analysis of the F1-Score is employed to assess the model's performance in further detail, using micro, macro, and weighted averages [28]. The macro F1-Score is the arithmetic average of the F1-Scores for each class, and the weighted F1-Score is the weighted average of the F1-Scores based on the proportion of classes. Additionally, the micro F1-Score is the average of the overall TP, FN, and FP. The weighted average is calculated by assigning weights according to the number of events, while the macro average treats all events equally by assigning them the same weight. Finally, the micro average allows us to determine the overall performance without any weighting for labels. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Activity** & **Fold1** & **Fold2** & **Fold3** & **Fold4** & **Development Dataset** \\ \hline Absence & 14,128 & 14,152 & 14,080 & 14,220 & 18,860 \\ \hline Cooking & 3840 & 3848 & 3804 & 3880 & 5124 \\ \hline Dishwashing & 1068 & 1080 & 1064 & 1060 & 1424 \\ \hline Eating & 1736 & 1736 & 1724 & 1728 & 2308 \\ \hline Other & 1540 & 1552 & 1544 & 1544 & 2060 \\ \hline Social activity & 3908 & 3896 & 3292 & 3736 & 4944 \\ \hline Vacuum cleaning & 732 & 708 & 744 & 732 & 972 \\ \hline Watching TV & 14,072 & 13,956 & 13,808 & 14,108 & 18,648 \\ \hline Working & 13,939 & 14,063 & 13,891 & 14,035 & 18,644 \\ \hline Total & 54,964 & 54,992 & 53,952 & 55,044 & 72,984 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of data instances for each label and fold in the cross-validation settings [19].