# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 7 Experimental Results
Although the Morlet wavelet in (4) is the most used and effective wavelet family in nonstationary audio analysis, we have also tested two other well-known and well-used wavelet families: the generalized Morse and the Bump wavelets [47], respectively. The results in terms of per-class precision, recall, and F1-score, and their related weighted averages, are shown in Table 9. From this table, we can argue that results of the generalized Morse wavelet are quite similar to those obtained by using the Morlet one (see Table 3). On the other hand, the results related to the Bump wavelet are slightly worse, even if they are quite good. The overall accuracies of these two approaches were 98.50% and 97.45%, respectively. These considerations confirm the effectiveness of the Morlet wavelet for analyzing audio signals in general and engine sounds in particular. Finally, in Table 10, we summarize the previous results of the proposed approach (last row) and the compared ones by considering some additional machine learning and deep learning approaches. Specifically, Figure 7 shows the accuracy of the compared approaches as a bar plot. Among the machine learning techniques, we considered the results obtained by using a Support Vector Machine (SVM), the k-Nearest Neighbors (k-NN), the Multilayer Perceptron (MLP), and a random forest. All of these approaches provided reasonable results [5], though the results were worse than those provided by deep learning techniques. Among these last methods, we also considered an approach based on a Deep Recurrent Neural Network (DRNN) that exploits different spectral features [54] and one based on a Deep Belief Network (DBN) that works on a statistical ensemble of different spectral features [33]. For the implementation details, we refer to the related references. The results reported in Table 10 and Figure 7 clearly show once again the effectiveness of the proposed idea, which can be considered an effective and reliable approach for classifying real-world signals recorded in construction sites. As a discussion, we can observe that scalograms generally capture salient localized events in sound frames, as shown by the horizontal lines or cloud-like points in Figure 3. In addition, the convolutional layers of the CNN are able to learn discriminative features, as shown in Figure 8, which, for example, shows the 256 feature maps of the fifth and last convolutional layer of the used architecture. Although single plots in the figure are quite small, it is clear that feature maps in the final layer are more specialized in detecting specific time scales. In fact, the scalogram in Figure 8 shows clear horizontal lines localized at a  
Figure 6: A visual comparison of all the compared approaches for the precision (**top**), recall (**middle**), and F1-score (**bottom**). specific scale. This kind of scale localization is typical of engines, a fundamental part of the machines considered in our dataset. This behavior justifies the better performance of the proposed approach for the classification of equipment sounds in construction sites. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Approach** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SVM [5] & 83.66 & 0.846 & 0.838 & 0.842 \\ \(k\)-NN [5] & 85.28 & 0.860 & 0.853 & 0.857 \\ MLP [5] & 91.06 & 0.913 & 0.932 & 0.923 \\ Random Forest [5] & 93.16 & 0.934 & 0.932 & 0.933 \\ Piczak [4] & 90.03 & 0.944 & 0.932 & 0.936 \\ DRNN [54] & 95.32 & 0.955 & 0.953 & 0.954 \\ DCNN [31] & 97.08 & 0.973 & 0.973 & 0.973 \\ DBN [33] & 97.79 & 0.978 & 0.978 & 0.978 \\ L-ESN+MV [34] & 95.26 & 0.957 & 0.953 & 0.952 \\ CNN [44] & 97.99 & 0.980 & 0.980 & 0.980 \\ CNN+SVM [46] & 97.79 & 0.978 & 0.978 & 0.978 \\ Proposed & 98.93 & 0.989 & 0.989 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of the compared approaches.