The SVD and reduced rank signal processing
Signal Processing - Theme issue on singular value decomposition
Structured subspace and rank reduction techniques for signal enhancement in speech processing applications
Savitzky-Golay smoothing and differentiation filter for even number data
Signal Processing
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
EURASIP Journal on Applied Signal Processing
A time--frequency approach for noise reduction
Digital Signal Processing
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
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
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This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. 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 leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods.