Research on vocal sounding based on spectrum image analysis

Abstract

The improvement of vocal singing technology involves many factors, and it is difficult to achieve the desired effect by human analysis alone. Based on this, this study based on spectrum image analysis uses the base-2 time-selection FFT algorithm as the research algorithm, uses the wavelet transform algorithm as the denoising algorithm, and combines comparative analysis to discuss the mechanism of vocal music, the state of vocalization, and the vocal quality of vocalists in vocal music teaching. Simultaneously, this study compares the singer's frequency, pitch, overtones, harmonics, singer formants, etc., and derives the characteristics of vocal vocalization under different conditions, and can be extended to all music vocal studies. Research shows that this research method has certain practicality and can provide theoretical reference for related research.

Application of Information Teaching in Vocal Music Assisted Practice in Mobile Internet Era

“…In this proposed model, the data processing and fusion in mobile internet are based on two conditions for providing recommendations. Further data fusion based on the sequential vocal music practices prevents different sessions under independent or joint data processing [as in equations ( 9) and (10)]. Therefore, the error detection is high compared to the other factors in vocal music teaching.…”

“…In equation (10), the vocal music teaching instance is based on user preference and switching ability where error detection is 0 and it does not identify any data processing and fusion from the mobile tutor application. Whereas 𝐸 𝐷 ≠ 0 and therefore, 𝐸 𝐷 ≤ 𝑀𝑇 𝑎𝑝𝑝 𝐴 𝑝 and the tutor assistance is recommended through neural network learning.…”

“…The humdrum toolkit is used for music information processing that provides various sets of training and lesson sessions for the music students. Humdrum finds out the necessary data for data processing that enhances the efficiency of the system [10].…”

A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology

“…There are many methods for extracting formants, such as the bandpass filter combination method, the spectrum reciprocal method, the LPC (Linear Predictive Coding) detection method and the AR(Augmented Reality) model detection method. After calculation and comparison, the AR method was selected in this experiment to extract the first and third formants (Sun, 2019). Because it provides a good vocal tract model (provided that the artistic voice samples are basically free of noise).…”