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• W3011647582 abstract "We propose an algorithm that exploits the benefits of sparse filtering and directional clustering when estimating under-determined mixing matrix from mixtures of sufficiently sparse sources. To express the direction of each sample by only a few vectors in which one vector is more dominant than the remaining ones, we propose to minimize the power mean of the magnitude-squared cosine distances between the estimated mixing matrix and the data. For the special case of estimating determined mixing matrix, we derive a stability condition for methods based on the magnitude-squared cosine metric. Our stability condition shows that the proposed approach, K-hyperlines, and sparse filtering can recover the invertible mixing matrix when the sources are i.i.d. super-Gaussian. Simulations using both synthetic data and recorded speech mixtures show that the proposed algorithm outperforms existing algorithms with lower computational complexity." @default.
• W3011647582 created "2020-03-23" @default.
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• W3011647582 date "2020-01-01" @default.
• W3011647582 modified "2023-09-23" @default.
• W3011647582 title "Directional Sparse Filtering for Blind Estimation of Under-Determined Complex-Valued Mixing Matrices" @default.
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• W3011647582 doi "https://doi.org/10.1109/tsp.2020.2979550" @default.
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