# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Results
#### 4.2.4 Comparison of Performance with Data Augmentation Technique Using Gaussian Noise  
We compared the proposed method with a method that generates data using Gaussian noise, which is a type of noise that can be included in a sound signal. We aimed to demonstrate that the effectiveness of the proposed method was not only due to the presence of noise. We generated Gaussian noise data by multiplying the Mel spectrogram by a Gaussian random value in the same way as in the data augmentation technique using RTFs. The experimental results in Table 6 indicated that Gaussian noise degraded the model's performance. Figure 9: Micro F1-Score by parameters. As demonstrated by the experimental results, Gaussian noise resulted in a lower F1-Score than the baseline model trained without any augmentation. In contrast, the proposed model increased the F1-Score compared to the baseline model and minimized the performance difference between the development and evaluation datasets. This result indicated that the model was normalized to some extent. Therefore, the proposed method demonstrated a different effect compared to simply adding noise. For further analysis, 100 samples of augmented data were generated for a known sound source (vacuum cleaner) using Gaussian distribution and the ratio of Rayleigh distribution. These were then compared with the spectrum of the sound source recorded by both known and unknown microphones. Figure 10a,b show the spectra of augmented data with each distribution, and in Figure 10c, the orange line represents the spectrum from the unknown microphone, while the blue line represents the spectrum from the known microphone. Figure 10 compares the spectra of sounds recorded from the same vacuum cleaner at different locations (with the known source taken from the development dataset and unknown source from the evaluation dataset) to those of the augmented data generated using Gaussian distribution and the proposed method. For statistical comparison, data augmentation using Gaussian distribution (Figure 10a) and the proposed method (Figure 10b) were performed on the same known data, thus generating 100 samples for each method. The spectrum was then calculated for each sample, and the results were sorted by order of magnitude for each frequency bin. Subsequently, lines were drawn at the 90 th, 75 th, 50 th, 25 th, and 10 th percentiles based on the magnitudes. In the case of Gaussian distribution, the spectral lines are more widely spread compared to the proposed method. In particular, for the 10% line, there is a significant difference in the shape of the spectrum compared to the original data. However, with the proposed method, the spectrum closely follows the shape of the original data, even at the 10% line. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Development Dataset** & \multicolumn{5}{c}{**Evaluation Dataset**} \\ \hline
**F1-Score** & **Micro** & **Macro** & **Weighted** & **Micro** & **Macro** & **Weighted** \\ \hline Without augmentation & 0.9001 & 0.8612 & 0.8974 & 0.8861 & 0.8397 & 0.8848 \\ \hline Gaussian noise & 0.8950 & 0.8475 & 0.8900 & 0.8873 & 0.8301 & 0.8845 \\ \hline RTFs (proposed) & **0.9060** & **0.8692** & **0.9037** & **0.8939** & **0.8433** & **0.8922** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of F1-Score of data augmentation technique using Gaussian noise and data augmentation technique using RTF. The highest score is indicated in bold. Figure 10: (**a**) Gaussian distribution and (**b**) the proposed method were used to create 100 augmented datasets from a known source. The spectra were then obtained and the data were sorted in the order of magnitude, where each line represents a different percentiles. (**c**) Spectra of the known source and an unknown source. As shown in Figure 9(c), the spectra of the known and unknown sources exhibit only slight differences.