Savitzky-Golay smoothing and differentiation filter for even number data
Signal Processing
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
EURASIP Journal on Applied Signal Processing
A high-resolution quadratic time-frequency distribution formulticomponent signals analysis
IEEE Transactions on Signal Processing
New insights into the noise reduction Wiener filter
IEEE Transactions on Audio, Speech, and Language Processing
Computer Methods and Programs in Biomedicine
Time-domain analysis of the Savitzky-Golay filters
Digital Signal Processing
Time domain signal enhancement based on an optimized singular vector denoising algorithm
Digital Signal Processing
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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.