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Titlebook: Independent Component Analysis and Signal Separation; 8th International Co Tülay Adali,Christian Jutten,Allan Kardec Barros Conference proc

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樓主: 平凡人
11#
發(fā)表于 2025-3-23 11:12:23 | 只看該作者
Conference proceedings 2009ized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applications, as well as a special session on evaluation.
12#
發(fā)表于 2025-3-23 17:50:55 | 只看該作者
0302-9743 are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applications, as well as a special session on evaluation.978-3-642-00598-5978-3-642-00599-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
13#
發(fā)表于 2025-3-23 19:35:41 | 只看該作者
Conference proceedings 2009held in Paraty, Brazil, in March 2009. The 97 revised papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applic
14#
發(fā)表于 2025-3-23 23:48:00 | 只看該作者
0302-9743 ICA 2009, held in Paraty, Brazil, in March 2009. The 97 revised papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, ot
15#
發(fā)表于 2025-3-24 05:55:48 | 只看該作者
Stationary Subspace Analysisn-stationary subspace. This factorization is a universal tool for furthering the understanding of non-stationary data. Moreover, we can robustify other methods by restricting them to the stationary subspace. We demonstrate the performance of our novel concept in simulations and present a real world application from Brain Computer Interfacing.
16#
發(fā)表于 2025-3-24 07:25:14 | 只看該作者
A Novel Subspace Clustering Method for Dictionary Design our dictionary design method offer considerable computational savings when compared to the K-SVD[1] for similar performance. Furthermore, the training method also offers better generalizability when evaluated on data beyond the training set, and consequently is well suited for continuously changing audio scenes.
17#
發(fā)表于 2025-3-24 13:39:36 | 只看該作者
18#
發(fā)表于 2025-3-24 15:32:53 | 只看該作者
Independent Component Analysis (ICA) Using Pearsonian Density Functionntropy is more exact than the classical approximation based on the polynomial density expansions when the source signals are supergaussian. The validity of the new algorithm is verified by computer simulation.
19#
發(fā)表于 2025-3-24 19:11:09 | 只看該作者
20#
發(fā)表于 2025-3-25 00:26:26 | 只看該作者
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