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Titlebook: Latent Variable Analysis and Signal Separation; 13th International C Petr Tichavsky,Massoud Babaie-Zadeh,Nadège Thirion Conference proceedi

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51#
發(fā)表于 2025-3-30 10:22:08 | 只看該作者
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發(fā)表于 2025-3-30 12:37:31 | 只看該作者
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發(fā)表于 2025-3-30 19:39:40 | 只看該作者
Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalizationa. It is shown that building tensors from this kind of data results in tensors with hidden block structure which can be recovered through the tensor diagonalization. The tensor diagonalization means multiplying tensors by several matrices along its modes so that the outcome is approximately diagonal
54#
發(fā)表于 2025-3-31 00:10:22 | 只看該作者
High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?this study is to find out which way is the best. We compare the state-of-the art methods N-D ESPRIT and IMDF, propose a modification of IMDF based on least-squares criterion, and derive expressions of the first-order perturbations for these methods. The theoretical expressions are confirmed by the c
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發(fā)表于 2025-3-31 04:36:39 | 只看該作者
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發(fā)表于 2025-3-31 08:31:36 | 只看該作者
57#
發(fā)表于 2025-3-31 12:42:18 | 只看該作者
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發(fā)表于 2025-3-31 14:37:55 | 只看該作者
Acoustic DoA Estimation by One Unsophisticated Sensorry of the underlying measurement space and show how it enables localizing white sources. Then, we extend the solution to more challenging non-white sources like speech by including a source model and considering convex relaxations with group sparsity penalties. We conclude with numerical simulations
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