# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods  
### Datasets  
There are very few publicly available datasets that contain monophonic singing (MIR-1K [21] and the Singing Voice Audio Dataset [22] for instance) and even fewer datasets that contain both spoken and singing speech. In fact, we have only found the NUS Sung and Spoken Lyrics Corpus [23] that is not completely suitable for our task because the files it contains are mono-class, i.e., they contain either speech or singing. Our goal is to separate speech segments from singing segments contained in the same recording, so we have used our Bertsolaritza database [24] to develop and train the algorithms. These algorithms have then been tested both in Bertsolaritza and NUS databases. The Bertsolaritza database contains 2095 Basque audio files from 187 different singers and has a total duration of 59 h, 10 min and 40 s. The whole database has been manually labelled to separate singing from speech, resulting in 53 h, 22 min and 38 s of singing voice and 5 h, 48 min and 2 min of speech. The metadata that come with the recordings have information about all the singer identities, but the host identity is only annotated if he or she also sings to introduce the topic for the verses. It was not feasible to manually label the identities of the rest of hosts, therefore we decided to use an approximate method to create the host labels for the metadata that were missing. Recordings are separated in sessions that correspond to different places and dates. We decided to assign the same speaker to all the recordings of one session, as usually there is only one host in each bertsolaritza show. We also classified the genre of the hosts by applying a threshold to the average value of the \(f_{0}\) in such a way that speakers with an average \(f_{0}\) value higher than the threshold are considered female and male, otherwise. The optimal threshold to define the genre of a host has been defined using the hosts whose identity was present in the metadata (31 different speakers in 54 sessions, with a total duration of 15 min and 5 s). Table 1 shows the classification results obtained using different thresholds, where 250 Hz is the value that gets best F-score, so we used this value to automatically label the genre of hosts in the recordings that lacked this information. With metadata available for all the singers and hosts, the final database contents are shown in Table 2. In most cases, speakers either sing or act as host, but, as we have already commented, some hosts give the topic for the improvised verses singing as well. These hosts appear in the recordings both singing and speaking. In the NUS database, each participant sings and reads the lyrics of four different well-known songs in English. There are 20 different songs in the database that includes 6 male and 6 female participants. All the participants are professional singers. Each recording contains either speech or singing voice and we used a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) based Voice Activity Detector (VAD) explained in Section 2.2.1 to obtain the voice segments and labelled them with the type of the recording. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold (Hz)** & **Precision** & **Recall** & **F-Score** \\ \hline
100 & 0.13 & 0.5 & 0.21 \\
150 & 0.66 & 0.63 & 0.44 \\
200 & 0.77 & **0.84** & 0.77 \\
250 & 0.88 & 0.77 & **0.81** \\
300 & **0.9** & 0.64 & 0.67 \\
350 & 0.37 & 0.5 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the automatic genre classification. Both databases have 44.1 kHz sample rate and have been downsampled to 16 kHz and converted to Windows Pulse Coded Modulation (PCM) files.