<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Tune is the essential music component. Melody extraction is a crucial technique in music retrieval. The pitching order of polyphonic music is the main feature of vocal music. This paper presents an improved method of extracting vocal themes based on the quadratic wave equation. This paper presents a new algorithm based on the frequency spectrum characteristics of verbal signals. This method can effectively reduce the amount of computation and shorten the time of the primary melody extraction. This paper proposes a new fundamental frequency recognition model based on BP neural network, which has a good ability for secondary wave transformation. This method can improve the recognition accuracy of the primary melody model while reducing the error of melody location. This can improve the overall vocal music of the leading melody extraction precision. The analysis results of MIR-1K data show that the overall recognition accuracy of the improved theme is 2.13% higher than that of the original method. Compared with the traditional way, the average time of theme extraction is shortened by 0.06 seconds.</p>
</abstract>

Tune is the essential music component. Melody extraction is a crucial technique in music retrieval. The pitching order of polyphonic music is the main feature of vocal music. This paper presents an improved method of extracting vocal themes based on the quadratic wave equation. This paper presents a new algorithm based on the frequency spectrum characteristics of verbal signals. This method can effectively reduce the amount of computation and shorten the time of the primary melody extraction. This paper proposes a new fundamental frequency recognition model based on BP neural network, which has a good ability for secondary wave transformation. This method can improve the recognition accuracy of the primary melody model while reducing the error of melody location. This can improve the overall vocal music of the leading melody extraction precision. The analysis results of MIR-1K data show that the overall recognition accuracy of the improved theme is 2.13% higher than that of the original method. Compared with the traditional way, the average time of theme extraction is shortened by 0.06 seconds.

Títul Automatic Extraction and Discrimination of Vocal Theme Based on Quadratic Wave Equation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Tune is the essential music component. Melody extraction is a crucial technique in music retrieval. The pitching order of polyphonic music is the main feature...

Títul Automatic Extraction and Discrimination of Vocal Theme Based on Quadratic Wave Equation

Publié en ligne: 06 juin 2023

Pages: 1983 - 1992

Reçu: 09 juin 2022

Accepté: 25 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00302

Mots clés
Quadratic wave equation, The extraction of the vocal theme, The salience of pitch, Sparse self-coding, Fundamental frequency determination

© 2023 Juan Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Títul Automatic Extraction and Discrimination of Vocal Theme Based on Quadratic Wave Equation

Publié en ligne: 06 juin 2023

Pages: 1983 - 1992

Reçu: 09 juin 2022

Accepté: 25 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00302

Mots clésQuadratic wave equation, The extraction of the vocal theme, The salience of pitch, Sparse self-coding, Fundamental frequency determination

© 2023 Juan Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Quadratic wave equation, The extraction of the vocal theme, The salience of pitch, Sparse self-coding, Fundamental frequency determination