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<p>In order to improve the recognition accuracy of symphonic music contour, this paper constructs an intelligent music main melody recognition system based on artificial intelligence technology to make melody recognition with certain search adaptation capabilities. Based on the traditional melody recognition system, the fundamental tone sequence of symphony fragments is obtained by using the fundamental tone extraction and short-time autocorrelation function in the melody contour feature extraction algorithm, which is transformed into the melody contour sequence after regularization and merging to determine the similarity of the music melody signal itself. The wavelet transform method and radial basis function algorithm are used to improve the defects of monophonic discrimination in the traditional recognition model so that the artificial intelligence technique can effectively fit with the symphony recognition model of music melody contour. The experiments show that: The average recognition accuracy of the AI-based music melody recognition system is 90.5%, which is significantly better than 69.5% of Sound Hunter software and 76.5% of Shazam software. For the five monophonic chords, the system’s recognition accuracy is as high as 98.3%, especially in the field of hanging chords with significant recognition effects. It can be seen that the artificial intelligence-based music main melody recognition system provides a scientific and authoritative recognition means for the dissemination and development of symphonic music and is conducive to improving the recognition accuracy of symphonic melodies.</p>
</abstract>

In order to improve the recognition accuracy of symphonic music contour, this paper constructs an intelligent music main melody recognition system based on artificial intelligence technology to make melody recognition with certain search adaptation capabilities. Based on the traditional melody recognition system, the fundamental tone sequence of symphony fragments is obtained by using the fundamental tone extraction and short-time autocorrelation function in the melody contour feature extraction algorithm, which is transformed into the melody contour sequence after regularization and merging to determine the similarity of the music melody signal itself. The wavelet transform method and radial basis function algorithm are used to improve the defects of monophonic discrimination in the traditional recognition model so that the artificial intelligence technique can effectively fit with the symphony recognition model of music melody contour. The experiments show that: The average recognition accuracy of the AI-based music melody recognition system is 90.5%, which is significantly better than 69.5% of Sound Hunter software and 76.5% of Shazam software. For the five monophonic chords, the system’s recognition accuracy is as high as 98.3%, especially in the field of hanging chords with significant recognition effects. It can be seen that the artificial intelligence-based music main melody recognition system provides a scientific and authoritative recognition means for the dissemination and development of symphonic music and is conducive to improving the recognition accuracy of symphonic melodies.

Reference: An algorithm for recognizing the main melody of orchestral music based on artificial intelligence of music melody contour

Applied Mathematics and Nonlinear Sciences

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

In order to improve the recognition accuracy of symphonic music contour, this paper constructs an intelligent music main melody recognition system based on...

Reference: An algorithm for recognizing the main melody of orchestral music based on artificial intelligence of music melody contour

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: -

Eingereicht: 01. Apr. 2022

Akzeptiert: 14. Sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00089

Schlüsselwörter
Melody recognition system, Artificial intelligence, Contour feature extraction algorithm, Short-time autocorrelation function, Fundamental tone sequence

© 2023 Weiwei Han, published by Sciendo

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

Reference: An algorithm for recognizing the main melody of orchestral music based on artificial intelligence of music melody contour

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: -

Eingereicht: 01. Apr. 2022

Akzeptiert: 14. Sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00089

SchlüsselwörterMelody recognition system, Artificial intelligence, Contour feature extraction algorithm, Short-time autocorrelation function, Fundamental tone sequence

© 2023 Weiwei Han, published by Sciendo

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

Schlüsselwörter
Melody recognition system, Artificial intelligence, Contour feature extraction algorithm, Short-time autocorrelation function, Fundamental tone sequence