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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.


关键词： 语音信号 矩阵分解 降噪算法 FIR滤波器 语音处理 IEEE TENCON 2013

主讲人：IEEE Fellow Prof. Søren Holdt Jensen 机构：Department of Electronic Systems of Aalborg University

时长：0:23:03 年代：2013年

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