## 4 Results
### Data Enrichment  
In this set of experiments, we focused on understanding the robustness of SpikeSireNet and evaluating on cross-data. Data augmentation techniques were proven to be beneficial in terms of providing robustness and generalization to new data and larger data samples. We evaluated the performance of the SpikeSireNet architecture in a cross-augmented dataset setting. The baseline model was trained on Siren dataset as in [18]. We explored further data enhancement by introducing noise signals with other environmental actors. Furthermore, SpikeSireNet was pre-trained on Siren dataset and validated on the Bird dataset [19] to understand how the proposed model generalizes to new data. In this case the bird songs were quite similar to the siren sounds but had a low frequency range. We further evaluated this sort of data augmentation on SpikeSireNet using three settings by varying the mel channels of the input features; however, to understand the effect of the spectral components, we used constant network hyperparameters. The dataset attributes are mentioned in Table 3.  
Figure 6 explains the accuracy obtained for each of the settings. Enriching data with various data augmentation techniques represents a true real-world scenario. This helps in real-time acoustic emergency detection task, and contributes towards model robustness to unknown environmental actors. The baseline dataset with siren + noise samples was trained, and the test accuracy was marked as purple circles (see Figure 6). The pre-trained \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\tau_{syn}\) & **h = w** & \(\tau=1\) s & \(\tau=2\) s & \(\tau=4\) s & \(\tau=10\) s & \(\tau=100\) s \\ \hline \multirow{4}{*}{1 s} & 4096 & 87.1\% & 87.8\% & 87.4\% & 85.2\% & 77.2\% \\  & 2048 & 89.2\% & 89.6\% & 89.3\% & 86.6\% & 77.5\% \\  & 1024 & 90.0\% & **90.5\%** & 85.1\% & 87.6\% & 77.9\% \\  & 512 & 88.2\% & 85.4\% & 85.2\% & 82.9\% & 77.5\% \\ \hline \multirow{4}{*}{2 s} & 4096 & 89.6\% & **90.9\%** & 87.8\% & 87.0\% & 77.7\% \\  & 2048 & 89.6\% & 88.9\% & 90.3\% & 88.7\% & 79.5\% \\  & 1024 & **90.6\%** & 88.7\% & **91.2\%** & 90.0\% & 81.1\% \\  & 512 & 84.3\% & 87.3\% & 87.0\% & 85.2\% & 77.1\% \\ \hline \multirow{4}{*}{4 s} & 4096 & 86.7\% & 88.0\% & 86.4\% & 87.5\% & 80.0\% \\  & 2048 & 83.2\% & 89.5\% & 88.1\% & 89.4\% & 82.0\% \\  & 1024 & 82.6\% & **90.4\%** & 89.4\% & **90.9\%** & 83.0\% \\  & 512 & 82.4\% & 85.8\% & 86.1\% & 86.3\% & 81.3\% \\ \hline \multirow{4}{*}{10 s} & 4096 & 85.1\% & 85.8\% & 85.9\% & 85.6\% & 80.2\% \\  & 2048 & 87.5\% & 88.1\% & 88.2\% & 87.4\% & 83.3\% \\  & 1024 & 89.4\% & 89.6\% & 89.7\% & 86.0\% & 85.6\% \\  & 512 & 84.0\% & 82.9\% & 83.1\% & 86.0\% & 83.6\% \\ \hline \multirow{4}{*}{100 s} & 4096 & 78.3\% & 77.7\% & 78.2\% & 78.8\% & 80.8\% \\  & 2048 & 80.3\% & 82.4\% & 82.5\% & 82.5\% & 83.2\% \\ \cline{1-1}  & 1024 & 84.3\% & 84.5\% & 85.3\% & 86.3\% & 81.7\% \\ \cline{1-1}  & 512 & 77.8\% & 77.3\% & 80.9\% & 80.3\% & 82.6\% \\ \hline \hline \end{tabular} Best accuracy is highlighted in bold.  
\end{table} 
Table 2: Accuracy values for time constant heterogeneity. \(\tau_{syn}\) is varied for different \(\tau=\tau_{mem}\) values across variation in window sizes 4096, 2048, \(\ldots\), 512 with no overlap and with \(n_{mel}=64\). Smaller time constants and larger windows tend to achieve accurate siren predictions. All experiments performed in this table are trained and validated on Siren dataset [18]. model was presented with the evaluation dataset consisting of samples from the Bird dataset introduced as a noise class (see Figure 6 marked by yellow triangles) alongside the siren dataset. With increasing mel bins, the performance improved. As the augmented dataset for evaluation was challenging because of the various noises and sounds like bird chirps, insect calls, rain, wind, etc., the performance increased only slightly. The distribution of noise and siren samples was uneven, and this might have impacted the performance.