Reduction of broad-band noise in speech by truncated QSVD

Jensen, Søren Holdt; Hansen, P. N.; Hansen, Søren; Sorensen, John A.

doi:10.1109/89.482211

Cited by 204 publications

(134 citation statements)

References 16 publications

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“…Simulation results show that the accuracy of target recognition is greatly improved as the received signals are first processed by our noise-reduction scheme. The SVD based concept has been utilized in speech [7,8] and imaging [9] signal processing. However, there is still no research that applies such a noise-reduction technique to radar target recognition.…”

Section: Introductionmentioning

confidence: 99%

Application of SVD Noise-Reduction Technique to Pca Based Radar Target Recognition

Lee¹,

Ou²,

Fang³

2008

PIER

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Abstract-The noise effect is very challenging in radar target recognition. It usually degrades the accuracy of target recognition and then makes the recognition unreliable. In this study, we present a noise-reduction technique to improve the accuracy of radar target recognition. Our noise-reduction technique is based on the SVD (singular value decomposition). The PCA (principal components analysis) based radar recognition algorithm is utilized to verify our noise-reduction scheme. In our treatment, the received signals are arranged into a Hankel-form matrix. This Hankel-form matrix is decomposed into two subspaces, i.e., the noise-related subspace and clean-signal subspace. The noise reduction is obtained by suppressing the noise-related subspace and retaining the clean-signal space only. Simulation results show that the accuracy of target recognition is greatly improved as the received signals are first processed by the SVD noise-reduction technique. With the use of proposed noise-reduction scheme, the radar target recognition can tolerate more noises and then becomes more reliable. The noise-reduction technique in this study can also be applied to many other problems in radar engineering.

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Section: Introductionmentioning

confidence: 99%

Application of SVD Noise-Reduction Technique to Pca Based Radar Target Recognition

Lee¹,

Ou²,

Fang³

2008

PIER

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“…The main advantage of the LS estimate is that one does not need consider any assumptions either about the signal or noise. For example, if the noise is not white, many other methods need prewhitening and dewhitening steps [16].…”

Section: Ls Estimate Of Smentioning

confidence: 99%

Singular spectrum analysis based on the minimum variance estimator

Hassani¹

2010

Nonlinear Analysis: Real World Applications

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Abstract-In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, we introduce the SSA technique based on the minimum variance estimator. We also consider the SSA technique based on the minimum variance and structured total least squares estimators in reconstructing and forecasting time series. A well-known time series data set, namely, monthly accidental deaths in the USA time series, is used in examining the performance of the technique. The results are compared with several classical methods namely, Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm.

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“…As long as A and B have the same number of columns and B has full column rank, the matrix quotient AB † represents a so-called prewhitened signal with white noise, to which the standard filtering and noise-reduction techniques can be applied; see [33] for details.…”

Section: The Rank-revealing Ullv Decompositionmentioning

confidence: 99%

“…The SVD detects near-rank deficiency in a matrix very reliably and yields all the necessary subspace information. Because the SVD algorithm is so reliable and numerically stable, it is used in a wide variety of applications, such as frequency estimation via least squares and total least squares [48,49,58], principal component analysis [60], noise reduction in speech processing [33], computer-aided geometric design [40], and information retrieval [4]. Additional applications of the SVD can be found in the International Workshop on SVD and Signal Processing proceedings [17,42,59].…”

Section: Introductionmentioning

confidence: 99%

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Fierro¹,

Hansen²,

Hansen³

1999

Numerical Algorithms

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We describe a Matlab 5.2 package for computing and modifying certain rank-revealing decompositions that have found widespread use in signal processing and other applications. The package focuses on algorithms for URV and ULV decompositions, collectively known as UTV decompositions. We include algorithms for the ULLV decomposition, which generalizes the ULV decomposition to a pair of matrices. For completeness a few algorithms for computation of the RRQR decomposition are also included. The software in this package can be used as is, or can be considered as templates for specialized implementations on signal processors and similar dedicated hardware platforms.

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Reduction of broad-band noise in speech by truncated QSVD

Cited by 204 publications

References 16 publications

Application of SVD Noise-Reduction Technique to Pca Based Radar Target Recognition

Application of SVD Noise-Reduction Technique to Pca Based Radar Target Recognition

Singular spectrum analysis based on the minimum variance estimator

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