A Snoring Signal Enhancement Algorithm Based on OM-LSA and Subspace

BinYi Lv, Tieqiang Li, Han Yang, Xia Li
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Abstract

In view of the noise reduction of the snoring signal, a snoring signal enhancement method is proposed in this paper, which is combined with the optimal modified logarithmic spectrum amplitude estimation (OM-LSA) and subspace method. Firstly, the OM-LSA algorithm integrating improved minimum control recursive average (IMCRA) is used for preliminary noise reduction. The method uses short-time window to estimate the minimum value of noise. It uses noise estimation to obtain the optimal spectrum gain function to minimize the mean square error between the actual pure snoring signal power spectrum amplitude and the estimated pure snoring signal power spectrum amplitude to suppress the noise. Then, the subspace method further reduces the noise to make a more compromised choice in suppressing noise and reducing signal distortion. The experimental results show that this method is better than most traditional speech enhancement algorithms in different noise environments and can obtain better snoring signal quality.
基于OM-LSA和子空间的打鼾信号增强算法
针对打鼾信号的降噪问题,本文提出了一种将最优修正对数谱幅估计(OM-LSA)和子空间方法相结合的打鼾信号增强方法。首先,采用改进最小控制递归平均(IMCRA)的OM-LSA算法进行初步降噪;该方法利用短时窗估计噪声的最小值。通过噪声估计得到最优的频谱增益函数,使实际纯打鼾信号功率谱幅值与估计纯打鼾信号功率谱幅值之间的均方差最小,从而抑制噪声。然后,子空间方法进一步降低了噪声,在抑制噪声和降低信号失真方面做出了更加折衷的选择。实验结果表明,该方法在不同噪声环境下优于大多数传统的语音增强算法,可以获得更好的打鼾信号质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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