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<p>In vocal teaching, the clarity and quality of sound signals are crucial for both teachers and students. However, the interference of background noise often affects the accuracy of sound signals, posing challenges to vocal teaching. To solve this problem, low-frequency ultrasound signal denoising processing technology has emerged, which has been widely used in multiple fields due to its efficient noise suppression ability. This study aims to explore the application of low-frequency ultrasound signal denoising technology in vocal teaching to reduce the interference of background noise. This article applies low-frequency ultrasound signal denoising technology to the field of vocal teaching, providing a new signal processing and optimization method for traditional vocal teaching. Based on data analysis, we have developed a comprehensive set of evaluation indicators, including signal-to-noise ratio improvement rate, mean square error, cross-correlation, and denoising technology effectiveness, to comprehensively evaluate the effectiveness of denoising processing. Through the application of Empirical Mode Decomposition (EMD) algorithm, we found that the signal generally exhibits a trend of linear descent and non-linear combination, revealing the decomposition characteristics of noise signals under different conditions, such as the exponential descent trend of pure noise decomposition curve and the U-shaped variation of noise signal decomposition curve. Research has shown that signals in empirical mode decomposition algorithms generally exhibit a trend of linear descent and nonlinear combination. The pure noise decomposition curve shows an exponential downward trend, and under the influence of high independent variables, the noise curve tends to stabilize. The decomposition curve of the noise signal shows a U-shaped change, and its final stabilization can reflect the intelligence of the empirical mode decomposition algorithm in reducing low-frequency signal noise under the action of small independent variables. The denoising processing indicators for low-frequency ultrasound signals include signal-to-noise ratio improvement rate, mean square error, cross-correlation, and denoising techniques. The root mean square error has the largest range of variation and shows an overall downward trend.</p>

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The Application of Low Frequency Ultrasonic Signal Denoising Processing Technology in Vocal Music Teaching

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Yiming Cui

摘要

In vocal teaching, the clarity and quality of sound signals are crucial for both teachers and students. However, the interference of background noise often affects the accuracy of sound signals, posing challenges to vocal teaching. To solve this problem, low-frequency ultrasound signal denoising processing technology has emerged, which has been widely used in multiple fields due to its efficient noise suppression ability. This study aims to explore the application of low-frequency ultrasound signal denoising technology in vocal teaching to reduce the interference of background noise. This article applies low-frequency ultrasound signal denoising technology to the field of vocal teaching, providing a new signal processing and optimization method for traditional vocal teaching. Based on data analysis, we have developed a comprehensive set of evaluation indicators, including signal-to-noise ratio improvement rate, mean square error, cross-correlation, and denoising technology effectiveness, to comprehensively evaluate the effectiveness of denoising processing. Through the application of Empirical Mode Decomposition (EMD) algorithm, we found that the signal generally exhibits a trend of linear descent and non-linear combination, revealing the decomposition characteristics of noise signals under different conditions, such as the exponential descent trend of pure noise decomposition curve and the U-shaped variation of noise signal decomposition curve. Research has shown that signals in empirical mode decomposition algorithms generally exhibit a trend of linear descent and nonlinear combination. The pure noise decomposition curve shows an exponential downward trend, and under the influence of high independent variables, the noise curve tends to stabilize. The decomposition curve of the noise signal shows a U-shaped change, and its final stabilization can reflect the intelligence of the empirical mode decomposition algorithm in reducing low-frequency signal noise under the action of small independent variables. The denoising processing indicators for low-frequency ultrasound signals include signal-to-noise ratio improvement rate, mean square error, cross-correlation, and denoising techniques. The root mean square error has the largest range of variation and shows an overall downward trend.