## 4 Results
### Effect of Overlapping Windows  
The focus of this work was to analyze and demonstrate the effect of sliding windows with and without overlaps on accuracy and onset latency for siren predictions. We further elaborated the results in terms of mel channel variations as depicted in different sub-figures in Figure 4.  
We expected that having more samples would increase the frequency density of the processed signal which allow us to have higher accuracy. Likewise, the introduction of overlaps should allow us to reduce the onset latency as the network would see new data more often. In alignment with our expectations and as evident from Figure 4, incorporating overlapping windows for the same window size helped to improve the training accuracy and our results showcased an increase in performance for overlaps within sliding windows. A slight drop in accuracy with smaller windows, and for their respective overlapping hop lengths was observed. This effect was observed in the window with sample size of 512 for hop length h = 0.5 w, which corresponded to a frequency range of 255 Hz. Siren sounds have a typical characteristic frequency of 400-600 Hz. The covered frequencies were below the signal of interest for window sizes below 256, thus explaining the slight drop. However, we need to keep in mind that we need to feed the input more frequently to the network, e.g., for \(h=0.5w\), the network needs to process the input twice as fast.  
This paper strengthens the idea of diversifying input features to obtain a range of performance variations for an acoustic scene analysis. The feature component was restricted to different bin sizes i.e., 32, 64 and 128 mel channels. The subplots in Figure 4 have mel channel variation indicated as the title of the plot. Smaller bin sizes indicate higher accuracy values albeit with a slight increase in onset latency to detect the siren sounds within the sound sample. This is intuitive and due to the fact that the feature set provided to the input of the SpikeSireNet has improved feature granularity, thus making details of spectral components prominent. Maintaining overlaps further emphasizes that there is little compromise on feature integrity; therefore, this explains the higher accuracy values for h = 0.5 w and h = 0.25 w than with no overlaps (h = w).  
We investigated the time to detect siren sounds using the onset of events by predicting the neuronal state change. This gave us an intuition of how the fine temporal resolution of the spiking neurons influenced latency. Hence, we designed experiments to vary the sliding window with overlap and fixed windows to measure the time to first event, given an audio sequence being processed and knowing the ground truth (label). We observed the latency values of validation samples in the last epoch and averaged them over the batch size.  
As observed in Figure 4b,d,e, the introduction of overlapping windows had a modest influence on latency within each window. It is our understanding that the hop length introduces faster processing, through a reduction in latency. Based on processing time alone, we expected 2\(\times\) reduction in latency h = 0.5 w, 4\(\times\) for h = 0.25 w. A latency improvement of 5\(\times\) was achieved for window size of 4096, this is explainable from the neuronal sensitivity to detect siren sound events in windows with increased information granularity.