Subspace-based Noise Reduction for speech Signals via Diagonal and triangular Matrix Decompositions(1)
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Subspace-based Noise Reduction for speech Signals via Diagonal and triangular Matrix Decompositions(1)
IEEE TENCON 2013——In this talk we survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our noise reduction 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. In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filterbank interpretations. The algorithms are illustrated with applications in speech processing.
嘉 宾 ：
IEEE Fellow Prof. Søren Holdt Jensen
视频年代：2013
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