# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 3 Results  
The networks were trained off-line using google-colab resources, where the final design was obtained by tuning the parameters using a grid search. At the end of each of the architectures, a softmax function is used to make the classification between the four possible sounds. When the networks are trained and the process of validation is completed, we made tests on the RaspberryPi.  
Before showing the experimental results on the proposed methods on the collected database, we compared the performance of the proposed system to the one achieved by the method presented in [8] to justify the use of CNN as classification model. The work in [8] is the closest research work to ours that we found in the literature, and it employs an SVM classifier with soft assignment on both the Mel spectrogram and MFCCs. To compare these methods, we applied our multi-headed 2D CNN to the database collected by them. This database was sampled at 32 KHz and has a sample resolution of 16 bits. Audios were recorded using a high-quality microphone. It contains three classes of audio events that have to be detected: screams, glass breaking, and gun shot. Background noise was artificially added to the audio files. A summary of the composition of their database is reported in Table 2, where the abbreviations GB, GS, S and BN correspond to Glass Breaking, gunshot, scream, background noise respectively.  
We also used the same evaluation criterion as in [8], in which an event is considered as correctly detected if it is detected in at least one of the sliding time windows that overlap with it. If in  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\  & **\# Events** & **Duration (s)** & **\# Events** & **Duration (s)** \\ \hline BN & - & 58,371.6 & - & 25,036.8 \\ GB & 4200 & 6024.8 & 1800 & 2561.7 \\ GB & 4200 & 1883.6 & 1800 & 743.5 \\ S & 4200 & 5488.8 & 1800 & 2445.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the composition of the dataset created in [8].  
Figure 6: Block Diagram of the designed Multi-Headed 2D Convolutional Neural Network (CNN).  
two consecutive time windows a foreground event is detected, a single false positive occurrence is counted [8]. We resampled audio data to 22,050 Hz. Tables 3 and 4 show the results on their database using SVM, as it was proposed in Table 3, and our CNN, respectively. Table 3 does not show results refereed to false positives for BN because the results were not reported in [8]. The average recognition rate of events achieved in [8] is 86.7% with a 2.6% of false positive rate, which is lower than the one achieved using our method which is 88.16% with a 0.91% of false positive rate. The CNN clearly outperforms a SVM in this classification task.  
Now that we justified the use of CNN as classification model, we evaluated the performance of the proposed architectures on our database. The validation process is based on a k-fold cross-validation scheme. Each fold contains the audio files from one person to guarantee independent samples in the training set. Then, we have 8 folds, each one containing 27 events for each class. The classification task is conducted on 450 ms time windows, with 112 ms of overlap. This window frame was selected considering that, to recognize distress signals, the window should be long enough to collect information that allows for discrimination. Taking this into account, every type of sound takes the same amount of time to process because all time windows are the same. To evaluate the classification models, we considered the recognition rate of each frame window and the false positive rate.