Generalized Subspace Snoring Signal Enhancement Based on Noise Covariance Matrix Estimation

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.