# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 3 Results  
### Parameter Sensitivity of the Note Detection Algorithm  
The note detection algorithm we explained in Section 2.2.2 has flexibility with respect to the characteristics of the notes it is detecting, namely different length and range values can be taken into account. In addition, different steps can be considered within each semitone. In our previous work [33], we used ideal semitones of a 440 \(A_{4}\) tuned scale to discretise the \(f_{0}\) curve because we think this procedure ensures enough definition to analyse the stability of the curve. However, the algorithm in Tony uses three steps per semitone for more precision in the detection of note levels [27]. This prompted us to include the number of semitone steps as a variable to be considered together with the minimum length and maximum pitch range. In order to check if these configuration parameters have an important impact in the detected notes, we devised an experiment to test the sensitivity of the results to these parameters. In this experiment, we applied our note detection algorithm with different minimum length, maximum range and steps per semitone to later discriminate speech and singing using the detected notes. We considered maximum ranges from 100 to 600 cents with intervals of 50 cents and minimum lengths from 50 to 450 ms with intervals of 50 ms. The dataset used is a Bertsolaritza database excerpt that was also used in [24]. To evaluate the classification, we used a 10-fold cross-validation with a joint F-score test. The F-score results with different note detection parameters are shown in Figure 6. We also tested a method that does not discretise the pitch curve before sequence searching and finds sequences using directly the values of the \(f_{0}\) curve. The F-scores obtained with this method are shown in Figure 7.  
Figure 6 shows that, when the note algorithm parameters get closer to the values common in Western music [34] (minimum duration in the range of 100-200 ms and maximum pitch range between 100 and 150 cents), the discrimination between singing and speech improves. This means that the optimum value for the parameters has a strong relation with the style of singing that we want to discriminate from normal speech. The vertical resolution in Figure 6 is half of the resolution shown in the rest of the cases because one step per semitone does not allow for having different results between semitones. We also tested the parameter setting where the possible semitone steps are infinite. This means that each sample in the \(f_{0}\) curve is a possible semitone, creating a continuity in the sequence search that can completely ignore the discrete semitone scale. This option can help in the case of analysing out of tune singing. The results obtained with continuous semitone steps are shown in Figure 7,  
Figure 5: Distribution of PV and PN parameters. (**a**) Bertsolaritza database; (**b**) NUS database. where a similar pattern to the one observed in the case of using the discretised \(f_{0}\) curve can be seen: the best F-score is obtained for the common values of minimum duration and maximum pitch range in Western music. The parameter combination that provides the best discrimination results for each of the different semitone step number is presented in Table 3. F-score improves as we use more semitone steps and the best result is obtained with continuous \(f_{0}\), which corresponds to a situation where infinite steps are considered. Nevertheless, the differences in F-score are slight and, providing that the maximum pitch range and minimum note length have values suitable for Western music, the number of steps considered has a small influence in the results. To apply the algorithm to our recordings, we have defined the maximum range of the notes as 100 cents, the minimum length as 150 ms, and we have considered four steps per semitone. Figure 6: Speech/singing classification F-score for different number of steps per semitone.