# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 1 Introduction  
Discrimination of speech and singing is not an easy task even for humans, who need approximately one-second-long segments to discriminate singing and speaking voices with more than 95% accuracy [1]. When speech is repeated, rhythm patterns appear and repeated spoken segments are perceived as singing [2, 3]. Even if singing and speech are closely related and difficult to be distinguished by humans, speech technologies developed for spoken speech are not directly applicable to singing speech. In general, results deteriorate heavily when using classic automatic speech recognition [4] or phonetic alignment [5] methods with singing speech. Therefore, when dealing with recordings that contain both types of speech, some tool must be designed to identify them in order to apply the technique suitable for each type of voice. Many works have addressed the problem of speech and music discrimination [6, 7, 8, 9], but these techniques are not directly applicable in the case of a capella singing because they exploit the presence of music. There are two different tasks related to the automatic discrimination of singing and speech: classification, when each segment (or file) belongs to only one class and segmentation where both classes are present in the same file and have to be first separated and then classified. For the classification step, short-term and long-term features extracted from the audio signal have been traditionally used. With the use of short-term features, the signal is classified at frame level and then decisions for different frames must be combined to obtain a single decision for each segment. Most works, however, rely on long-term parameters related to pitch to discriminate speech andsinging. For instance, distribution of different pitch based features was tested in [10] and most of them achieved correctly classifying more than half the database. The best results in this work were obtained for autocorrelation of pitch between syllables. Pitch and energy related features were also fed to a multi-layer Support Vector Machine (SVM) in [11] and the pitch related features contributed the most to the discrimination. [PERSON] [12] proposed to use the discrete Fourier transform of the pitch histogram to estimate the distribution of pitch deviations that should have lower variance for singing than for speech and obtained very good results. Some works combine short-term features related with the spectral envelope and long-term features related with prosody to train Gaussian Mixture Models (GMM) and distinguish speech and singing. In [1], the authors found that short-term features work better for segments shorter than 1 s and pitch related features obtain the best results for segments longer than 1 s. On the contrary, the work in [13] finds that spectral features work better than pitch based ones when used alone in their database composed of segments between 17 and 26 s. Regardless, they obtained the best results when combining both types of features. In [14], a large set of 276 attributes related with spectral envelope, pitch, harmonic to noise ratio and other characteristics and a ensemble of classifiers are proposed to classify singing voice, speech and polyphonic music and get very good results. As mentioned before, a related problem that has been studied in more depth is the separation of the singing voice from the background music. In this area, different strategies have been applied. Many works are based on the use of pitch related information. For instance, in [15] and with the goal of melody extraction, the use of a joint detection and classification network to simultaneously estimate pitch and detect the singing voice segments in a music signal is proposed. Pitch information is also exploited in an iterative way to detect singing voice in a music signal using a tandem algorithm in [16].