# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Implementation Details  
We obtained eight types of abnormal sounds, namely coughing, cracking fire, crying baby, glass breaking, gunshot, sneezing, snoring, and screams by combining the publicly available datasets ESC-50 and UrbanSound8k, which were used as input data for detecting abnormal sound samples. In the experiments, we set the training data to test data as 4:1--for every 4 samples trained, 1 test was conducted--and the optimal model and parameters were obtained by five-fold cross validation. The comprehensive information about data augmentation is shown in Table 1. These data were divided into eight categories, with 37 samples per class for gunshot and screams in the base dataset and 40 samples per  
Figure 5: EANN for sound events recognition. class for the other six sounds. Augmentation was directly applied to the eight sound data categories to generalize and improve data quality. For single data augmentation methods like pitch shifting, add background noise, and time stretching, the augmented data were increased fivefold. With the combination of pairwise data augmentation methods, the augmented data were increased tenfold. Each type of original audio data augmentation scheme was combined with online data augmentation for experimentation. All augmented samples required labeling of the augmentation type to determine which augmentation method could maximize recognition accuracy. In comparison to the existing data augmentation techniques of Time Stretching, Pitch Shifting, and Background Noise, the proposed combination data augmentation method was evaluated. The input features for MFCC were configured with the following parameters: f_max = 14,000, f_min = 50, hop_length = 256, n_fft = 1024, n_mels = 128, n_mfc = 40, sample_rate = 16,000, and win_length = 1024. The model optimizer used was AdamW, with an initial learning rate of 0.001, a learning rate decay weight of \(1\times 10^{-6}\), and a dropout rate of 0.5. We used several state-of-the-art evaluation metrics to investigate the performance of the classification task, namely Accuracy, Macro Precision, Macro Recall, Macro Specificity, Macro F1-Score, and the confusion matrix. Calculating the above metrics requires an understanding of four key concepts: True Positives (TPs), which are instances that are positive and correctly predicted as positive; False Negatives (FNs), referring to instances that are positive but incorrectly predicted as negative; False Positives (FPs), indicating instances that are negative but incorrectly predicted as positive; and True Negatives (TNs), which are instances that are negative and correctly predicted as negative. The descriptions for the performance metrics used in this study are presented in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Class** & **Baseline** & **Signal DA** & **Combined DA** \\ \hline coughing & 40 & 200 & 400 \\ cracking\_fire & 40 & 200 & 400 \\rying\_baby & 40 & 200 & 400 \\ glass\_breaking & 40 & 200 & 400 \\ gun\_shot & 37 & 185 & 370 \\ sneezing & 40 & 200 & 400 \\ snoring & 40 & 200 & 400 \\ screams & 37 & 185 & 370 \\ Total & 314 & 1570 & 3140 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of datasets before and after data augmentation.