# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 3 Results
### Speech and Singing Discrimination  
We have compared our algorithm with other methods training them in our Bertsolaritza database and using the NUS database for test purposes. We have selected methods that are suitable to work with segments of different duration as it is the case of Bertsolaritza database. We have trained three different GMM classifiers: one with the first difference of \(f_{0}\) as suggested in [1] (\(\Delta f_{0}\)), another with the distribution of the Discrete Fourier Transform (DFT) of \(f_{0}\)[12] (DFT-\(f_{0}\)) and the last one with MFCC parameters (MFCC). The details of the calculation of these methods are explained in [33]. In addition, we have also considered using note labels from Tony instead of our note detection algorithm for the calculation of \(PN\) parameter (Tony). In the proposed system, the statistical classifiers used as last stage have been the SVM [36; 37] and the Extreme Learning Machine (ELM) [38; 39]. None of these methods consider parameters related with the duration of the segment to classify, even in an indirect way. They use normalised histograms, percentage values and majority votes, which are parametrizations \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Steps per Semitone** & **Minimum Duration (s)** & **Maximum Range (Cents)** & **F-Score** \\ \hline
1 & 0.15 & 100 & 0.882 \\
2 & 0.2 & 100 & 0.896 \\
3 & 0.15 & 150 & 0.902 \\
4 & 0.15 & 100 & 0.902 \\
5 & 0.15 & 100 & 0.900 \\
6 & 0.15 & 150 & 0.905 \\ Continuous & 0.2 & 100 & **0.908** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best result of each semitone step division level. Figure 8: Histogram of kappa score between notes detected by [PERSON] and our algorithm. that do not depend on the duration of the segment and allow the classification of segments with different length. The performance of the classifiers has been measured using unweighted Precision, Recall and F-score, defined as the macro-averaged measure parameter for each of the classes. We used unweighted mean of the score because our Bertsolaritza database is heavily imbalanced, with more singing segments than speech segments as seen in Section 2. Macro-averaging considers all classes equally and, in the case of imbalanced datasets, is more convenient [40]. We have used the 5x2 cv paired \(t\) test [41] with the structure shown in Figure 9 to compare the systems. To achieve this, we split the Bertsolaritza database in 10 sections with the only condition that all the segments of each speaker had to be in a single section. After this, we made five iterations of splitting the database in two blocks of five random sections with no block repetition. In each of the iterations, we created a first score by using one block as train and the other as test. A second score is calculated by rotating test and train, training set becomes test and vice versa. This gives us 10 scores in total (two per iteration) that are used to calculate the means and variance values presented in Tables 4 and 5 and the \(p\) scores in Table 6. The procedure ensures that no speaker is present both in training and test block in any iteration. To test the generalisation capability of the algorithm, the NUS database has been classified using the algorithms trained with all the Bertsolaritza database and without any special adaptation. The results are shown in Tables 4 and 5 for the classification of singing and speech, respectively. Results using a SVM classifier are presented for our proposed method because it performed better than the EML classifier. GMM trained with MFCCs is the best method in the Bertsolaritza database, although differences in results with our proposed method are not statistically significant (see Table 6). When we apply this model to NUS database, it gets very good results for the singing class, comparable to our method. In the Bertsolaritza database, singing and speech are mixed in the same file, while, in the NUS database, each file contains only one type of voice.