## Appendix A  
In this section, some representative examples of anomalous noise events selected from both scenarios (urban and suburban) are shown in terms of their spectral and \(LA_{eq}\) behaviours for illustrative purposes. The purpose of the following examples is to show that real-life recordings poses a serious challenge for ensuring a reliable database labelling process. In the following examples, the A-weighted equivalent noise level using 30 ms as integration time (\(LA_{eq}\)) is computed. Also, perceptual-based spectrograms are derived from the Gammatone Cepstral Coefficients (GTCC) parameterization of the input audio signal, by using a Gammatone filterbank of 48 subbands within the available frequency range at 48 KHz, a frame length of 30 ms with Hamming window and with overlap of 50% between consecutive frames [42]. In the figures, the ANEs start and end time boundaries are represented as magenta vertical dashed lines, and their labels and SNRs obtained from the labelling process described in Section 3.3 are also superimposed in both \(LA_{eq}\) curves and spectrograms. In Figure 11, an example of different ANEs recorded in the Milan urban environment is depicted. The recording was obtained in a two-way street with two lanes each way in the Milan city center, specifically close to the traffic lights of a crossroad and within a shopping area. As it can be observed, the excerpt covers up to 14 s of audio in which four types of anomalous noise events are highlighted: two events of people talking ('people'), a car horn ('horn'), three door-like sound ('door') and a car brake ('bra'). Four ANEs with low (or very low) saliency can be observed: the car brake sound (\(\text{SNR}=0.4\) dB) two doors (SNRs of 1.5 and 1.6 dB) and one sound of people talking (\(\text{SNR}=1.2\) dB). Also two intermediate saliency ANEs (people sound event with \(\text{SNR}=4.1\) dB and a door sound with \(\text{SNR}=3.8\) dB) and a high saliency ANE can be seen (car horn with \(\text{SNR}=7.6\) dB). In this example almost all the ANEs are quite identifiable directly from the \(LA_{eq}\) trajectory: there is a clear pulse-like waveform that comprises the main parts of the ANE, which emphasizes its salience with respect to the background traffic noise. However, the non-stationary nature of the sound sources poses a challenge to compute the ANE contextual SNR reliably. In the case of sound of people talking, vowels usually have a higher \(LA_{eq}\) than consonants. Contrarily, door-like sounds are very impulsive and their acoustic energy is highly local. In Figure 12, another example of two ANEs with different salience (intermediate and high) observed in the urban scenario within a low traffic density one-way little road in Milan is shown. Their respective SNRs are shown under their four-character label ('airp' and 'tran'). As it canbe observed, two cars pass-bys are present between the two ANEs, which constitutes a highly non-stationary background. In this example, it is evident that traffic noise and the two ANEs exhibit \(LA_{eq}\) trajectories that can be hardly distinguished, while GTCC-based spectral distributions could contribute to better represent their differences. Figure 14 shows an example of anomalous noise events with intermediate, low or very low saliency and road traffic noise +collected in the suburban scenario (Rome ring highway). Two sirens are observed with different SNRs (2.4 and 1.4 dB), also two horns with different SNRs (3.9 and \(-\)0.3 dB) and the sound of portal structure movement when a big truck pass-by (with SNR \(=-\)0.3 dB). Compared to the previous examples recorded in Milan's urban scenario, in this example the analyzed ANEs contextual SNRs present lower values. While the two ANEs with highest SNR (first siren and first horn) can be identified from the \(LA_{eq}\) curve, the other three events with low or very low saliency remain quite unnoticed. Figure 14: Examples of a mid-salient ANE (airplane, with SNR \(=\) 2.1 dB) and a highly-salient ANE (train, with SNR \(=\) 9.3 dB) in the urban scenario.