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

<p>In this paper, the extracted acoustic features are processed using the MFCC method to classify the sound recognition. The linear spectrum of the sound is mapped onto the Mel FPP coefficients and Mel nonlinear spectrum, respectively, and the MFCC is obtained by cepstrum processing. The Mel FPP coefficients and Gaussian mixture model are combined to create the acoustic model. On this basis, the art and emotional skills of vocal singing are explored, and simulation and empirical experiments are set up to analyze the effect of the model constructed in this paper. The experimental results show that the model constructed in this paper tends to stabilize when the number of iterations is 2, the final accuracy rate stabilizes near 0.9, and the model is effective. Different vocal types are recognized by the model constructed in this paper, and the recognition rate of emotional expression of all vocal types is above 0.7, and the model recognition rate is high. The accuracy and recall classification for emotional expression techniques are 0.73 and 0.79, resulting in a more balanced evaluation overall.</p>
</abstract>

In this paper, the extracted acoustic features are processed using the MFCC method to classify the sound recognition. The linear spectrum of the sound is mapped onto the Mel FPP coefficients and Mel nonlinear spectrum, respectively, and the MFCC is obtained by cepstrum processing. The Mel FPP coefficients and Gaussian mixture model are combined to create the acoustic model. On this basis, the art and emotional skills of vocal singing are explored, and simulation and empirical experiments are set up to analyze the effect of the model constructed in this paper. The experimental results show that the model constructed in this paper tends to stabilize when the number of iterations is 2, the final accuracy rate stabilizes near 0.9, and the model is effective. Different vocal types are recognized by the model constructed in this paper, and the recognition rate of emotional expression of all vocal types is above 0.7, and the model recognition rate is high. The accuracy and recall classification for emotional expression techniques are 0.73 and 0.79, resulting in a more balanced evaluation overall.

Analysis of the Art and Emotional Skills of College Vocal Singing in the Age of Big Data

Applied Mathematics and Nonlinear Sciences

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

In this paper, the extracted acoustic features are processed using the MFCC method to classify the sound recognition. The linear spectrum of the sound is...

Analysis of the Art and Emotional Skills of College Vocal Singing in the Age of Big Data

Publié en ligne: 23 déc. 2023

Pages: -

Reçu: 06 mars 2023

Accepté: 03 juil. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01588

Mots clés
MFCC algorithm, Acoustic modeling, Mel-freepeak coefficient, Voice recognition, Vocal singing

© 2023 Fan Zhang, published by Sciendo

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

Analysis of the Art and Emotional Skills of College Vocal Singing in the Age of Big Data

Publié en ligne: 23 déc. 2023

Pages: -

Reçu: 06 mars 2023

Accepté: 03 juil. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01588

Mots clésMFCC algorithm, Acoustic modeling, Mel-freepeak coefficient, Voice recognition, Vocal singing

© 2023 Fan Zhang, published by Sciendo

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

Mots clés
MFCC algorithm, Acoustic modeling, Mel-freepeak coefficient, Voice recognition, Vocal singing