Extension of the signal subspace speech enhancement approach to colored noise

Lev-Ari, H.; Ephraim, Yariv

doi:10.1109/lsp.2003.808544

Cited by 95 publications

(42 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2, orthogonal projection is depicted, where a noise eigenvector u n is decomposed into two orthogonal projections such that u np is along and u ⊥ np is perpendicular to . In matrix form, the orthogonal projection U np of the eigenvector matrix U n of R n along is given by (13).…”

Section: Additive Noise Removalmentioning

confidence: 99%

“…In matrix form, the orthogonal projection U np of the eigenvector matrix U n of R n along is given by (13).…”

Section: Additive Noise Removalmentioning

confidence: 99%

“…Hu and Loizou [9] presented a TDC estimator based on the joint diagonalization of the covariance matrices of the clean signal and the noise process. Lev-Ari and Ephraim [13] extended their original speech enhancement subspace approach to coloured-noise processes in the time and spectral domains using whitening of the input noise. Pre-whitening and De-whitening [14], use of a common diagonalization matrix [15], use of Rayleigh quotient method [16] etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Signal Subspace Speech Enhancement with Oblique Projection and Normalization

Surendran¹,

Kumar²

2017

RADIOENGINEERING

View full text Add to dashboard Cite

Section: Additive Noise Removalmentioning

confidence: 99%

“…In matrix form, the orthogonal projection U np of the eigenvector matrix U n of R n along is given by (13).…”

Section: Additive Noise Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Signal Subspace Speech Enhancement with Oblique Projection and Normalization

Surendran¹,

Kumar²

2017

RADIOENGINEERING

View full text Add to dashboard Cite

“…These conventional algorithms are suitable for white noise reduction. To deal with colored noise, one conventional approach is that the noisy speech signal is multiplied by the square root of the noise covariance matrix's inverse [14]. Another conventional approach is the prewhitening covariance matrix of the colored noise.…”

Section: Introductionmentioning

confidence: 99%

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Tu¹,

Xia²

2016

JCC

View full text Add to dashboard Cite

Time delay estimation (TDE) is an important issue in signal processing. Conventional TDE algorithms are usually efficient under white noise environments. In this paper, a joint noise reduction and p l -norm minimization method is presented to enhance TDE in colored noise. An improved subspace method for colored noise reduction is first performed. Then the time delay is estimated by using an p l -norm minimization method. Because the clean speech signal form the noisy signal is well extracted by noise reduction and the p l -norm minimization method is robust, the TDE accuracy can be enhanced. Experiment results confirm that the proposed joint estimation method obtains more accurate TDE than several conventional algorithms in colored noise, especially in the case of low signal-to-noise ratio.

show abstract

“…Extended approaches (Hu and Loizou, 2003), (Lev-Ari and Ephraim, 2003) exploit a joint diagonalisation of the noise and clean speech covariance matrices which allows for a derivation of the optimal estimators for the coloured noise. Unfortunately closed form expressions (Lev-Ari and Ephraim, 2003) for these estimators are rather impractical.…”

Section: Introductionmentioning

confidence: 99%

Signal subspace approach for psychoacoustically motivated speech enhancement

Borowicz

Petrovsky

2011

Speech Communication

View full text Add to dashboard Cite

To cite this version:Adam Borowicz, Alexandr Petrovsky. Signal subspace approach for psychoacoustically motivated speech enhancement. Speech Communication, Elsevier : North-Holland, 2010, 53 (2) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Signal subspace approach for psychoacoustically motivated speech enhancement AbstractIn this paper we deal with the perceptually motivated signal subspace methods for speech enhancement. We focus on extended spectral-domain-constrained (SDC) estimator. It is obtained using Lagrange multipliers method. We present an algorithm for a precise computation of the Lagrange multipliers, allowing for a direct shaping the residual noise power spectrum. In addition the SDC estimator is presented in a new, possibly more effective form. As a practical implementation of the estimator we propose perceptually constrained signal subspace (PCSS) method for speech enhancement. The approach utilizes masking phenomena for residual noise shaping and is optimal for the case of coloured noise. Also, less demanding approximate version of this method is derived. Finally comparative evaluation of the most known subspace-based methods is performed using objective speech quality measures and listening tests. Results show that the PCSS method outperforms other methods providing high noise attenuation and better speech quality.

show abstract

Extension of the signal subspace speech enhancement approach to colored noise

Cited by 95 publications

References 6 publications

Signal Subspace Speech Enhancement with Oblique Projection and Normalization

Signal Subspace Speech Enhancement with Oblique Projection and Normalization

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Signal subspace approach for psychoacoustically motivated speech enhancement

Contact Info

Product

Resources

About