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

<p>As computer science and technology continue to evolve and become more pervasive, their application in analyzing the audio spectrum of vocalizations offers valuable insights for vocal music education. This study introduces a method utilizing Fourier transform analysis to examine time-frequency domain signals in vocal teaching. Initially, voice frequencies are collected during vocal music instruction. Subsequently, these frequencies are processed to extract characteristic sequences, which are then reduced in scale to develop a model for voice spectrum recognition tailored to vocal music education. This model facilitates detailed spectral analysis, enabling the investigation of its auxiliary benefits in vocal music teaching, particularly in identifying prevalent instructional challenges. Our findings indicate that during training on vowels “a” and “i,” professional singers’ pitch at 4kHz declined to between −15 and −18 dB, whereas students’ pitch varied around ±6dB, trending upwards. In cases of air leakage, significant gaps were observed at frequencies of 5500Hz, 10500Hz, and 14500Hz. At the same time, students exhibited missing frequencies at 7kHz, 12kHz, and 14kHz during glottal tone production, with pronounced, abrupt peaks occurring when vocal folds were tightly constricted and devoid of excessive links. This research substantiates the theoretical and practical benefits of digital spectrum technology in enhancing vocal music education, thereby providing a scientific and supportive role.</p>
</abstract>

As computer science and technology continue to evolve and become more pervasive, their application in analyzing the audio spectrum of vocalizations offers valuable insights for vocal music education. This study introduces a method utilizing Fourier transform analysis to examine time-frequency domain signals in vocal teaching. Initially, voice frequencies are collected during vocal music instruction. Subsequently, these frequencies are processed to extract characteristic sequences, which are then reduced in scale to develop a model for voice spectrum recognition tailored to vocal music education. This model facilitates detailed spectral analysis, enabling the investigation of its auxiliary benefits in vocal music teaching, particularly in identifying prevalent instructional challenges. Our findings indicate that during training on vowels “a” and “i,” professional singers’ pitch at 4kHz declined to between −15 and −18 dB, whereas students’ pitch varied around ±6dB, trending upwards. In cases of air leakage, significant gaps were observed at frequencies of 5500Hz, 10500Hz, and 14500Hz. At the same time, students exhibited missing frequencies at 7kHz, 12kHz, and 14kHz during glottal tone production, with pronounced, abrupt peaks occurring when vocal folds were tightly constricted and devoid of excessive links. This research substantiates the theoretical and practical benefits of digital spectrum technology in enhancing vocal music education, thereby providing a scientific and supportive role.

The use and effective analysis of vocal spectrum analysis method in vocal music teaching

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

As computer science and technology continue to evolve and become more pervasive, their application in analyzing the audio spectrum of vocalizations offers...

The use and effective analysis of vocal spectrum analysis method in vocal music teaching

Published Online: Jun 03, 2024

Page range: -

Received: Feb 12, 2024

Accepted: Apr 14, 2024

DOI: https://doi.org/10.2478/amns-2024-1361

Keywords
Spectrum analysis, Fourier transform, Audio feature sequence, Vocal spectrum recognition, Vocal music teaching

© 2024 Bo Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The use and effective analysis of vocal spectrum analysis method in vocal music teaching

Published Online: Jun 03, 2024

Page range: -

Received: Feb 12, 2024

Accepted: Apr 14, 2024

DOI: https://doi.org/10.2478/amns-2024-1361

KeywordsSpectrum analysis, Fourier transform, Audio feature sequence, Vocal spectrum recognition, Vocal music teaching

© 2024 Bo Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Spectrum analysis, Fourier transform, Audio feature sequence, Vocal spectrum recognition, Vocal music teaching