## 4 Results
### Discussion  
We trained a recurrent SNN in different experimental setups to detect siren sounds on a modified public dataset from [18] to firstly understand the impact of sliding windows on the task performance and secondly, to provide a basis for the correlation between sliding windows and the leaky behavior of spiking neurons employed in SpikeSireNet. Our results indicated a performance boost in terms of accuracy using sliding windows with overlaps. Figure 6: **Validation on data induced with various environmental actors. ** Purple circles indicates accuracy obtained for testing on Siren dataset [18] and yellow triangles indicate evaluation accuracy on augmented siren data with bird dataset [19]. SpikeSireNet shows a similar performance with data augmentation and new unseen samples in the audio sequences. Mean accuracy and standard deviation over 10 random seeds is reported. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Siren Dataset**[18] & **Bird Dataset**[19] \\ \hline \multirow{2}{*}{Class} & Siren sounds (wait, yelp, hi-lo), traffic noise & Bird audio, noise, insect calls, white noise, wind, human speech, rain \\ \hline Average Clip Duration & \(\approx\)3 to 15 s & 3 to 30 s \\ \hline Total Duration & \(\approx\)7 h & 50 h \\ \hline Sampling Rate & 48 kHz & 44.1 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset Attributes. This was intuitive as with the enlarged features (high data points) that bacame available with the overlaps, the features were also retained, hence the accuracy was higher. With overlaps, the feature retention was increased and translated to less probability of missing information. Interestingly, a higher overlap ratio for hopping windows in smaller window regimes showed the best performance with 92.4 \(\pm\) 0.8% accuracy for M = 32 and 64, closely matching the performance for larger window sizes. We particularly noted slight drop in accuracy with smaller windows below our signal of interest (siren sounds). We conducted experiments with different mel channels keeping the network parameters like hidden size and readout constant. The overall performance was better for 128 mel channels in terms of accuracy, also reaching the best onset latency. From our experimental results, a trade-off was achieved with 64 mel channels sufficing to predict accurate siren sounds for the proposed SpikeSireNet. Therefore, we conclude that sliding windows with an overlapping windows translate into improved task performance. Collectively, our results appear consistent with the body of literature that show the impact of sliding windows for various detection tasks like HAR [20], and pose pattern recognition task [23]. We further believe this is one of the first works to demonstrate a sliding window variation for an acoustic anomaly task, specifically using a spiking architecture. Some authors have shown the significance of time constants of spiking neuron models to help improvise the performance of spatio-temporal pattern recognition task [26]. Neurons tend to show the behavior which confirms that leakages exist in neuronal models as underpinned by biology and by authors in [24; 25]. The relation between the leaky behavior of spiking neurons in terms of exponential decay as time constants and that of the sliding windows was understood step-by-step through experiments in this work. At a higher level of abstraction we understood the variation of sliding windows and the impact on onset latency and accuracy for various time constants (in this case, keeping \(\tau_{mem}=\tau_{syn}\)). With smaller neuronal time constants, the response time of neurons was faster. The obtained prediction accuracy was highest for overlapping windows and thus led to faster processing speed. It is noteworthy, as the time constants fell in the range of audio sequences, this adversely impacted the accuracy by nearly 10%. We also noted that for smaller time constants and smaller windows, accuracy and onset latency were 5.5% higher and 2.4\(\times\) lower, respectively, in contrast to higher time constants.