## 3 Environmental Noise Database
### Real-Life Urban and Suburban Environmental Audio Database Generation  
After finishing the recording campaign, a post-processing phase was conducted in order to normalize and label all the recorded audio files and export them into analyzable audio clips. To that effect, we used the Audacity freeware software, generating a total of eighteen audio projects, one for each session during the recording campaign. Six projects were related to the suburban recordings, and twelve to the urban recordings, and their amplitude was normalized by using the calibration tone. Finally, each audio event was manually detected and labelled (including start and end points) by an expert annotator to guarantee the reliability of the annotation. The expert annotator was asked to annotate only those ANEs perceptually distinguishable from the background road traffic noise or from other acoustic events simultaneously occurring in the acoustic mixture. Specifically, and after an initial listening revision, the expert was asked to distinguish between three major categories: road traffic noise (RTN, assigned to all audio regions containing road transit), background noise (BCK, reserved to those recordings where it was difficult to identify the noise coming from vehicles since they contain the background noise of the city) and ANEs. Anomalous noise events were subsequently labelled into 19 subcategories, taking into account the diversity of the acoustic phenomena gathered during the real-life environmental recording campaign. These subcategories were defined to enrich the description and subsequent analyses of the collected acoustic occurrences for both acoustic environments (urban and suburban). The labels were inspired in the taxonomy defined in [34] (specifically devoted to urban acoustic environments), although it was not fully adopted since specific noises were identified beyond the ones proposed in that taxonomy (e.g., the noise derived from the portals' structure vibration in the suburban recordings). Concretely, the following labels were agreed within the DYNAMAP consortium to annotate the ANEs collected in both urban and suburban scenarios:  
* _airp_: airplanes. * _bike_: noise of bikes. * _bird_: birdsong. * _brak_: noise of brake or cars' trimming belt. * _bused_: opening bus or tramway, door noise, or noise of pressurized air. * _chains_: noise of chains (e.g., bicycle chains). * _dog_: barking of dogs. * _door_: noise of house or vehicle doors, or other object blows. * _horn_: horn vehicles noise. * _mega_: noise of people reporting by the public address station. * _musi_: music in car or in the street. * _people_: people talking. * _sire_: sirens of ambulances, police, fire trucks, etc. * _stru_: noise of portals structure derived from its vibration, typically caused by the passing-by of very large trucks. * _thun_: thunder storm. * _tram_: (stop, start and pass-by of tramways). * _tran_: (stop, start and pass-by of trains). * _trck_: noise when trucks or vehicles with heavy load passed over a bump. * _wind_: noise of wind, or movement of the leaves of trees. Subsequently, the labelled audio clips were exported as independent '.wav' audio files using a sampling rate of 48 KHz and 16 bits/sample. Each filename contained the following parts: type of sensor, type of event, order of appearance of this type of event in the same audio project, direction of measurement in relation with the traffic direction, elevation angle of the measurements, type of road and traffic density. Subsequently, the ANE audio chunks were also tagged in terms of SNR in dB, that is computing the relative amount of ANE amplitude with respect to the BCK or RTN noise. After a preliminary attempt to compute this calculation manually, we opted to develop a semiautomatic SNR labelling approach, which is explained in Section 3.3. Table 1 shows the final inventory of audio files, indicating their durations for each of the two considered recording environments.