# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation  
### Evaluation Settings  
To validate our algorithm, we selected the DCASE2018 Task5 dataset, specifically the SINS dataset [6]. Owing to the planned data collection process in the SINS dataset, we could clearly verify the performance gap between the known and unknown microphones, and we anticipated confirming the efficacy of our proposed technique. Furthermore, while SED detects multiple events within each audio segment with the onset and offset times, MDA detects only a single event, making it simpler to analyze the performance of the proposed algorithm. The SINS dataset [19] consists of 4-channel audio data, including living rooms, workrooms, kitchens, and dining rooms. Data were recorded with four microphone bundles at seven locations, and the development dataset recorded 10 s of data from the four microphones, for a total of 200 h. The SINS dataset was labeled with nine activities: absence, cooking, dishwashing, eating, other, social activity, vacuum cleaning, watching TV, and working. The dataset was divided into 4-fold cross-validation [6], where the data were split into four equal subsets. Each subset was used once as a validation set while the remaining \begin{table}
\begin{tabular}{l l} \hline Room size [x, y, z] & [7, 4, 2.3] m \\ \hline Receiver position [x, y, z] & random \\ \hline Source Position [x, y, z] & [2, 2.5, 1] m \\ \hline Sound velocity & 340 m/s \\ \hline Reverberation time & 0.4 s \\ \hline \end{tabular}
\end{table} 
Table 1: Simulated data RIR generator [21] parameters. Figure 7: **(a)** is a PDF of simulated and generated data, and **(b)** is the histogram. The solid line in **(a)** and orange bars in **(b)** represent the PDF of the ratio distribution generated by the RIR model [21], while the dashed line in **(a)** and purple bars in **(b)** correspond to random numbers simulated using (11). three subsets were used for training. In cross-validation, a fold refers to a bundle consisting of one validation set and the remaining three subsets. The model structure used for training was originally proposed in [13]. The overall model structure and output size of each layer, as depicted in Table 2, consisted of simple CNNs. The model input was a 10 s audio segment at a sampling rate of 16 kHz, which was converted to STFT through 1024 FFT points and filtered with Mel-filterbank with 40 mel filters. The features were normalized to values between 0 and 1. We set the learning rate to 0.0001, batch size to 128, and used Adam [26] as the optimizer and categorical cross-entropy as the loss function. Figure 8 shows the intermediate results of the feature extraction process, where the input signal is transformed from its time-domain waveform to a frequency-domain mel-spectrogram. By applying the mel filter bank to the spectrogram, the scale of the low-frequency components is increased as human hearing is significantly emphasized in this region. This allows the model to be trained with more detailed low-frequency information. We applied our algorithm to the mel-spectrogram to generate multiple augmented data, which were then used as input for the proposed system. For comparing the proposed method, datasets processed with different augmentation algorithms were trained in the same model structure [13]. The first was mix&shuffle, which was used by the first-place team in the competition [13], and proved to be a simple and effective technique. In addition, specaugment [14] and mixup [27], which are recent methods that are commonly used in audio augmentation, were used as comparison groups. We augmented data for labels with less than 4000 data in each fold to balance the dataset, except for the mixup [27]. The mixup was applied during the dataset loading process. Data augmentation was applied until the dataset reached a total of 4800 samples. Table 3 presents the number of samples per fold and label before data augmentation.