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10 items by relevance
1 Report

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

Hansen, Per Christian; Jensen, Søren Holdt

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both

Year: 2006

Language: English

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damfg e jchikobnlp
2 Journal article

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis

Sø, Jensen; Holdt, ren; Per Christian, Hansen

Eurasip Journal on Advances in Signal Processing — 2007

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV, and ULLIV). In addition, we show how the subspace-based algorithms can be analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated...

Year: 2007

Language: English

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hmg cifno lae bpkjd
3 Journal article

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis

Hansen, Per Christian; Jensen, Søren Holdt

Eurasip Journal on Applied Signal Processing — 2007, Volume 2007, Issue 1, pp. 1-24

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both

Year: 2007

Language: English

More...
fei n bghoakdclm pj
4 Journal article

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis

Sø, Jensen; Holdt, ren; Per Christian, Hansen

Eurasip Journal on Advances in Signal Processing — 2007

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV, and ULLIV). In addition, we show how the subspace-based algorithms can be analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated...

Year: 2007

Language: English

More...
alk cihno pjgdfmeb

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