# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 4 Discussion  
In this article, we present a novel method of discriminating speech and singing segments using only two parameters derived from \(f_{0}\). The system has been tested and compared with two systems based also on \(f_{0}\) analysis and another one based on spectral information in two databases with different characteristics. The proposed method gets better results than the other systems using pitch parameters and equivalent results compared to the spectrum analysis system. It has been also proven that the proposed one generalises the classification in other databases without bias to the singing class and has a lower computation time. We observed that the two parameters used for the classification in the proposed system, PV and PN, are very discriminative in databases with diverse characteristics: different languages, singing proficiency levels, recording conditions and quality, etc. These parameters are also capable of classifying voice segments of short length and this provides a high flexibility to the classification system. As a by-product, an algorithm to detect notes in a singing voice has been created. The algorithm is easy and intuitive to tune. It only uses three configuration parameters that are directly related with the characteristics of a specific music style, in this case, the Western music. The test has shown that the algorithm is able to cope with audio files containing music and speech segments. It is capable of obtaining musical information from the singing voice without trying to classify speech as singing. Other existing note detection algorithms are tuned to deal with music signals and therefore they try to find notes in all the segments they process. On the contrary, our algorithm has been designed expressly for detecting notes in signals with speech and music and it is not biased to any of the classes. We compared our note detection algorithm with the state-of-the-art algorithm of [PERSON] and we have seen that the agreement between the two methods regarding note detection is strong. The parameter sensitivity analysis of the note detection algorithm showed that changes in the minimum duration have more effect on the speech/singing classification results than the maximum pitch range. If the maximum pitch range considered is high, all the pitch variations in the signal would be included in the range and then the only parameter with influence on the results has a minimum length. When the minimum length considered is very small, the pitch variation inside the segment usually is not very large and then all segments are treated as musical notes, which gives poor speech/singing classification results regardless of the maximum pitch range applied. In general, the singing voice has a greater pitch range than speech, but it has smaller local pitch changes once the vibrato has been smoothed in the pitch curve. For future work, we consider it interesting to apply the note labelling algorithm to different music styles and datasets and find the optimal values for the configuration parameters valid for other singing styles. It would be also very interesting to develop new objective measures for the evaluation of note detection algorithms. In addition, we are considering the possibility to test the potential of LSTMs for performing the note detection. Conceptualization, X.S., D.T. and E.N. ; Methodology, X.S. and L.S. ; Software, X.S. and D.T. ; Validation, X.S. and E.N. ; Formal analysis, X.S., D.T. and L.S. ; Investigation, X.S. ; Resources, X.S., L.S. and I.S. ; Data curation, X.S. ; Writing--original draft preparation, X.S. and E.N. ; Writing--review and editing, X.S., E.N., I.S. and I.H. ; Visualization, X.S. and E.N. ; Supervision, E.N., I.S. and I.H. ; Project administration, E.N., I.S. and I.H. ; Funding acquisition, E.N., I.S. and I.H.