## 5 Experimental Results
### Cascade Approach
#### 5.4.2 Feature Selection  
The selection of the audio features to be used by the classifiers can be done using the Weka audio analysis tool. The Weka's features' selection uses the ranker algorithm _InformationGain_, which gives the set of most relevant features per dataset. This information can be used to select the best feature set leading to the highest classifier performance [1]. The main concern is to have the highest performance while using the smallest number of features as possible. Similar to [1], three sets of features have been created and used to identify the feature set leading to a higher classifier's performance. Each set has been created based on:  
* **Set 1** : Features that are common to all datasets. * **Set 2** : Features that are common to at least three datasets. * **Set 3** : All features provided by LibROSA. Table 7 summarizes the features composing the sets under evaluation. The common features to all datasets of the hierarchical dataset are expected to be the most useful. Nevertheless, it resulted in a very short set composed of only two features. Our analysis shows that more features are in common between several datasets, and, in order to increase the number of features to be evaluated, Set 2 and Set 3 are proposed. Whereas Set 2 is composed of features that are also common to at least three datasets, Set 3 is expected to deliver the best results since all supported features are used. Although a more fine-tuning selection of the features can be done by evaluating the linear correlation among the features, as done in [1], we have decided to only evaluate the feature sets depicted in Table 7 for the sake of simplicity. Table 8 summarizes the classifiers' performance (with default configuration) for each feature set. The values in bold correspond to the highest performance reached for one feature set. The classifiers k-NN, Naive Bayes and ANN achieve higher performance for most of the datasets. Most of the classifiers increase their performance when increasing the number of features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Set 1** & \multicolumn{2}{c}{**Set 2**} & \multicolumn{2}{c}{**Set 3**} \\ \hline
**Features** & **Time [ms]** & **Features** & **Time [ms]** & **Features** & **Time [ms]** \\ \hline Mcc 1, 6 & 17.30 (1.08) & Zero Crossing & 4.17 (0.37) & Mcc 0 - Mcc 12 & 17.30 (1.08) \\
**Total time ( \(t_{extraction}\) )** & 17.30 (1.08) & Sp. Contrast 0, 2 & 24.58 (1.92) & Sp. Contrast 0 - Sp. Contrast 5 & 24.58 (1.92) \\  & & Mcc 0, 2, 4, 5, 7 & 17.30 (1.08) & Sp. Centroid & 12.75 (0.54) \\  & & **Total time ( \(t_{extraction}\) )** & 46.06 (0.62) & Sp. Roll off & 12.93 (0.55) \\  & & & Sp. Bandwidth & 15.84 (0.59) \\  & & & Rms & 63.95 (3.70) \\  & & & Zero Crossing & 4.17 (0.37) \\  & & & **Total time ( \(t_{extraction}\) )** & 151.55 (1.67) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Considered feature sets and average time required for the features’ extraction. The values in brackets are the Standard Deviation (SD). Figure 9: Detailed hierarchical dataset for the cascade approach. always the case. For instance, the k-NN and the Decision Tree classifiers do not present significant difference on its performance for the _Construction_ dataset. Nonetheless, for most of the cases, Set 3 achieves the highest performance. As a result, this feature set is used hereby for our evaluations. The average execution time of each classifier for each features set is depicted in Figure 10. On the one hand, the classifier k-NN demands a computational time significantly higher than other classifiers, while the ANN classifier demonstrates being extremely fast independently of the number of features. On the other hand, not all classifiers present the same sensitivity to the feature's set.