基于多窗谱减和LMS在工厂中的去噪实现
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内蒙古科技大学信息工程学院,包头 014000

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TN912

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内蒙古自然科学基金(2021MS06030)、内蒙古科技攻关项目(2021GG0023)资助


Implementation of Denoising in Factory Based on Multi-Window Spectral Subtraction and LMS
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School of Information Engineering , Inner Mongolia University of Science and Technology, Baotou 014000, China

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    摘要:

    为有效抑制工厂复杂环境中的背景噪声,获取音频信号中包含的有用信息,提出了一种基于多窗谱谱减法和自适应最小均方误差滤波算法相结合的音频降噪方法。首先使用改进多窗谱谱减法即修改谱减关系中的增益因子对含噪音频进行初步噪声抑制,有效避免了音乐噪声的产生并提升在了非平稳噪声干扰下的音频感知质量。然后再使用基于双曲正切函数调整步长因子的变步长自适应LMS滤波算法对已经初步去噪后的音频信号进行二次降噪处理,从而达到消除音频中噪声分量目的。仿真实验结果表明,本文方法相较传统多窗谱谱减法去噪后信噪比提升7dB左右,较固定步长LMS算法提升3至4dB,较传统多窗谱级联定步长LMS算法提升1至2dB,且该方法简单易行,且具有较好的实际应用价值。

    Abstract:

    In order to effectively suppress background noise in the complex environment of the factory and obtain the useful information contained in the audio signal, an audio noise reduction method based on the combination of multi-window spectral subtraction and least mean square filtering algorithm is proposed. First use the improved multi-window spectrum subtraction, the gain factor in the modification of the spectrum reduction relationship is used to suppress the noise-free frequency, effectively avoid the production of music noise and increase the audio perception of non-smooth noise interference. Then use the changeable step-by-side adaptive LMS filtering algorithm based on the double-type normal cleaning function to adjust the step-up audio signal that has initially denoising the audio signal, thereby achieving the purpose of eliminating the noise component in the audio. The simulation experiment results show that this method is less than about 7dB of the signal-to-noise ratio of 7dB compared to the traditional multi-window spectrum reduction, and the fixed step LMS algorithm is increased by 3 to 4 dB, and the LMS algorithm of the traditional multi-window calibration step is increased by 1 to 2dB, and this method is simple and easy to have a good practical application value.

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徐金石,杨立东.基于多窗谱减和LMS在工厂中的去噪实现[J].电子测量技术,2021,44(24):66-71

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  • 在线发布日期: 2024-07-02
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