## 5 Discussion
### Implications of the Results  
In general terms, the conducted statistical analyses confirm that the two chosen scenarios (urban and suburban) exhibit different patterns in terms of both background noise and anomalous noise events, e.g., showing significant differences in terms of types of ANE and SNRs between them; a result that could be expected a priori. However, beyond specific aspects related to the different characteristics of these soundscapes (e.g., the impulse-like vs. stationary nature of noise events), it is reasonably to remark that both soundscapes entail a large variability and diversity of ANE in terms of occurrences, durations and SNRs. Regarding background noise, the suburban recordings contain mainly continuous road traffic noise, while the urban recordings also include other kinds of road noise like those gathered from interrupted traffic (e.g., crossroad with traffic lights) or the background city noise observed in low-density traffic city roads. Concerning the ANEs, the urban scenario entails a larger diversity of anomalous noise events than the suburban scenario, while the latter presents ANEs with lower contextual SNRs. Another interesting aspect derived from the analyses is that real-life recordings reflect a highly unbalanced nature between anomalous noise events (uncommon sounds) and road traffic or background city noise (common sounds). In the 9 h 8 min of labelled audio database, the ANE subset represents 3.2% of the total recorded audio in the suburban scenario, while in the urban soundscape it comprises 12.2%. This fact can be of paramount importance when it comes to developing sound event detectors or classifiers, as the training and testing processes can be highly influenced by the ratio between the amount of examples of the classes to be identified [28; 41]. Furthermore, the limited presence of ANEs in real-life recordings also makes it very difficult to model of such diversity of sound events within individual classes (i.e., one class per event typology). This issue can be alleviated by integrating all types of ANE within the same class, as will be done in the DYNAMAP project to discard them from the road traffic noise computation. Furthermore, we can also conclude that the generation of synthetic sound mixtures either from online databases or real isolated ANE by considering predefined SNRs as proposed in previous works is still far from what can be observed in both real-life urban and suburban scenarios, which entail a dramatically larger variability. Concretely, ANE variability has been observed both in terms of the number of occurrences per ANE and the diversity of their durations, ranging from almost 0 s for impulse-like sounds such as dogs barks or closing vehicle doors, to more than 30 s for music or sirens. Moreover, the mean SNR values in both real-life urban and suburban recordings also show a significantly larger range of values (from \(-10\) to \(+15\) dB) with respect to previous artificially generated audio databases (e.g., \(\{-6,-3,0,+3,+6\}\) dB [22] or \(+6\) dB plus \(+12\) dB [21]), with relevant diversity of intermediate SNR values. In our opinion, this fact is of paramount importance since the acoustic salience of a sound event could highly affect the performance of any automatic recognition system as already observed in [28]. However, this hypothesis should be further studied in future works. Finally, although the recording campaign was designed to collect a large diversity of anomalous noise events, it is worth mentioning that the obtained results should not be understood as a generalizable result to any urban or suburban scenario as the recorded database comprises a large but limited set of labelled audio recordings of 9 h 8 min. At best, the obtained general patterns could be observed in very similar urban and suburban environments. Nevertheless, the main conclusions and considerations discussed in this paper can be taken into consideration for similar studies.