# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 2 Materials and Methods
### Data Processing
#### 2.2.2 Recognition of the Noise Event Source  
The ANED algorithm, designed in the framework of the DYNAMAP (dynamic noise mapping) project [28], is a two-stage decision scheme, as shown in Figure 5. At the first level, ANED classifies the acoustic signal at a frame-level base. It is based on a two-class detection-by-classification [29] approach, using an artificial intelligence algorithm trained by means of acoustic models from representative real-life data collected from both suburban and urban environments. In order to generate the frame-level label every 30 ms, the acoustic signal is analyzed by means of a feature extraction stage. This short-term windowing of the acoustic signal is performed by means of a Hamming window of 30 ms, with an overlap between windows of 50% of the signal, followed by a feature extraction step based on the Mel-frequency cepstral coefficient (MFCC) [30]. This feature extraction is followed by a Gaussian mixture model (GMM)-based binary classification stage [28]. The second level, a high-level decision, based on a majority vote criterion, had to be conducted every second, and it considered the aggregated values of the frame-level decisions, in a predefined time window, to generate a binary output (RTN/ANE). Both levels require a real-time implementation in-situ in the sensor gathering the data in order to provide the final binary label at the moment of the L\({}_{\text{Aeq}}\) evaluation. The ANED algorithm was trained using more  
Figure 4: Example of plots obtained from the data processing setting time gap between events \(\tau_{\text{g}}\) = 5 s and event duration \(\tau\) > 2 s. Red and black dots mark events due to road traffic and other sources, respectively; blue cross mark event with not assigned source: (**a**) algorithm “IR”; (**b**) algorithm “Lr”; (**c**) algorithm “L50”; (**d**) algorithm “O10”.  
than 150 h of expert-manually labeled data, coming from the 24 sensors, deployed by the DYNAMAP project in Milan, with around 8% of anomalous noise events (e.g., bird singing, sirens, dogs barking, horns, trams). The diversity coming from 24 sensors--some from narrow streets, others in wide ones, some close to parks, and others close to schools--gave the dataset a variate group of examples of urban sounds. The ANED procedure was already tested and validated in a real environment in the framework of the LIFE DYNAMAP project [28; 31; 32]. A further study [29] showed an accuracy of more than 70% in urban environment ANE detection, measured at frame level (using a 30-ms window analysis), by means of a 10-fold cross-validation test to ensure the stability of the results. The performance of the ANED algorithm was also validated in other environments [18], considering, in addition, the acoustic salience of the ANEs in real-operation [33]. The validation was inspired on the manual evaluation process by experts, considering the most salient events present in the measurements, with respect to the L\({}_{\text{Aeq1s}}\). The algorithm detected--for the events catalogued as high-salience--all of the present airplane noise, more than 90% of works, and people talking, and for the mid-high salience, more than 84% of airplane noise, nearly 80% of works, and more than 60% of people talking, to list a few examples. The accuracy results of the ANED algorithm is especially improved with a high-level stage, and it shows good performance, especially when dealing with high salience events.