Acoustical properties of snoring signal have been widely studied as a potentially cost-effective and reliable alternative to diagnosing obstructive sleep apnea hypopnea syndrome, with a common recognition that the diagnostic accuracy depends heavily on the snoring signal quality. In the paper, generalized subspace noise reduction based on noise covariance matrix estimate is proposed. The noise covariance matrix is the Toeplitz matrix of the unbiased autocorrelation sequence which is estimated by recursive averaging its past value adjusted by a time-varying smoothing parameter controlled by the snoring signal presence probability, and the signal presence is determined by the ratio of temporal frame autocorrelation value to its minimum absolute value. The proposed method has a better estimate of noise covariance matrix, and the results of objective quality measurements and spectrograms of snoring signal show obvious improvement in terms of noise reduction and signal distortion under different non-stationary noise environments compared with conventional subspace enhancement algorithm.

打鼾信号的声学特性已被广泛研究，作为诊断阻塞性睡眠呼吸暂停低通气综合征的潜在成本效益高且可靠的替代方法，人们普遍认为诊断准确性在很大程度上取决于打鼾信号的质量。本文提出了基于噪声协方差矩阵估计的广义子空间降噪。噪声协方差矩阵是无偏自相关序列的托普利兹矩阵，它是通过递归平均其过去值估计的，该值由打鼾信号存在概率控制的时变平滑参数调整，信号存在由时间帧的比率决定自相关值与其最小绝对值。所提出的方法对噪声协方差矩阵有更好的估计，