As part of the study, the problem of developing and applying a metric for assessing the degree of similarity of time series is considered, which makes it possible to consider the assumptions about the model of a series when comparing, as well as to compare the values of the corresponding characteristics of the series. Characteristics can be values that describe the structure of a series, or directly the values of the approximating function, which can be obtained using nonparametric statistics methods. One of the directions in which this approach can be applied to assessing the similarity of time series is the study of vocal performances. In practice, the degree of similarity in the performance of melodies by several speakers was analyzed. It was determined that, using the synchronicity metric, it is possible to implement an exercise in which students need to repeat the melody after the teacher. In addition, this approach was applied in the segment identification module with an abrupt change in the sounding of the fundamental frequency. This work is devoted to the modification of the program complex for vocal recognition in order to identify notes with a sharp change in the fundamental frequency. The complex is aimed at carrying out additional independent training in teaching vocals. The use of the software package will allow, in real time, providing feedback to the user with an assessment of the quality of their singing. This should allow students to study not only under the supervision of a teacher, but also independently in the early stages of learning. The basic algorithm of the program recognizes notes without sharp changes in frequencies with high accuracy, which is confirmed by experiments. In order to recognize by the algorithms of the program notes sung vibrato and glissando in singing, a new analysis method based on the metric of time series synchronicity is proposed.

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The paper presents the results of signal processing using the simplest methods of time series analysis to localize the main note in the presence of vibrato or ascending / descending glissando. The idea of using smoothing to find the vibrato effect is to evaluate segments of the speech signal, within which it was not possible to identify the note. Data on frequencies in this segment are averaged to localize the main note, relative to which the deviation in sound and the frequency of changes are determined in percentage terms. If the deviation from the main note is more than half a tone, the frequencies are perceived as noise.

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