A time–frequency approach for noise reduction

This paper proposes a technique for reducing noise from a signal's time series using a time–frequency distribution. The technique is based on the SVD of the matrix associated with the time–frequency representation of the signal. In this approach the time–frequency representation of the signal is initially divided into signal subspace and noise subspace using singular values of the time–frequency matrix as a criterion for space division. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix enhances the information embedded in the time–frequency representation of the signal. The proposed approach utilizes the Savitzky–Golay low-pass filter for noise attenuation from the singular vectors. The results of applying the proposed method on both synthetic signals and newborn EEGs indicate superiority of the proposed technique over the existing one in reducing noise from signals.