## 5 Experimental Results
### Cascade Approach
#### 5.4.1 Hierarchical Dataset  
The existing datasets do not necessarily contain the desired categories for a particular application. The generation of new datasets takes effort and is a time-demanding task. Instead, existing datasets can be combined or rearranged in order to satisfy the application's demands. In order to solve the limitations present on the existing datasets, a semantic hierarchical dataset has been generated (Table 6). The datasets described in Section 3.1.1 are used here to create the new dataset, which is used to evaluate our approach in Section 5.4.4 and in Section 5.5.3. The new dataset is built under certain conditions in order to have higher performance:  
* **The dataset must contain relevant categories**: This work intends to evaluate ML techniques in recognizing urban sounds around the campus of the Vrije University Brussels (VUB). Therefore, categories such as _Rivers_ or _Sea waves_ are discarded. Other similar categories are merged into the same category. For example, the existing categories _Cars_ and _Motorcycles_ in datasets are merged into a new category _Vehicles_. Figure 6: Achieved accuracy of the classifiers with their default and optimized configuration. Figure 7: Relative improvement of optimised parameters over default parameters of the classifiers. * **Small number of categories per data set**: According to [1; 34; 36], there is an inverse relation between the number of categories contained in a dataset and the classifier's performance. The more categories, the lower the classifier performance that is obtained. Although the cascade approach intends to minimize this degradation, the number of supercategories should be up to 5 each [36]. Notice that, in spite of up to 25 categories able to be recognized thanks to using the cascade approach, the hierarchical dataset only presents 12 categories of interest. * **Balanced categories**: All supercategories contained in the hierarchical dataset should be equally balanced. This condition is satisfied by forcing each supercategory to present an equal number of audio samples per category. * **Sufficient number of audio samples per category**: The data set should have a sufficient number of audio samples per category in order to train the classifier. For the generation of the hierarchical dataset, some online tools such as Freesound [37] and Freesoundeffects [38] have been used to increase the number of audio samples per category. Figures 8 and 9 illustrate the semantic hierarchical dataset for the cascade approach. This approach uses multiple datasets as categories for the initial stage. The hierarchical dataset is composed of five _supercategories_, which are in fact standalone datasets: _Vehicles_, _Warning_, _Residence_, _Construction_ and _Airport_. The hierarchical dataset can be considered as composed of six datasets; these five _supercategories_ and the complete dataset without categories (_Root dataset_) used at the initial stage (Figure 8). All are created by combining the datasets described in Section 3.1.1. Based on the prediction of the first stage, a second stage recognizes, the categories of the recognized _supercategory_ to which the sound belongs (Figure 9). As a result, the cascade approach \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Categories** & **Audio Data (s)** \\ \hline \multirow{3}{*}{Root Dataset} & Airport & 475 \\  & Traffic & 475 \\  & Construction & 475 \\  & Residence & 475 \\ \hline \multirow{3}{*}{Airport} & Airplane & 395 \\  & Helicopter & 395 \\ \hline \multirow{3}{*}{Construction} & Drilling & 170 \\  & Jackhammer & 170 \\ \hline \multirow{3}{*}{Vehicles} & Cars & 455 \\  & Motorcycles & 455 \\ \hline \multirow{3}{*}{Warning} & Cars horn & 345 \\  & Siren & 345 \\ \hline \multirow{3}{*}{Residence} & Cats & 505 \\  & Human & 505 \\ \cline{1-1}  & Children & 505 \\ \cline{1-1}  & Dogs & 505 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the categories used in the hierarchical dataset. Figure 8: Training dataset for the first stage of the cascade approach.