#### DMCA

## Blind extraction algorithm with direct desired signal selection

### Citations

219 |
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
- Cichocki, Amari
- 2002
(Show Context)
Citation Context ...l processing problems, e.g., the cocktail party problem. Many approaches to solve this problem use Blind Signal Processing (BSP) techniques. In BSP problems the source signals as well as the mixing system are unknown. Therefore, BSP techniques generally use no training data and no a priori knowledge about the mixing system. However, an essential problem in BSP is the permutation problem, which implies that signals can be separated or extracted in an arbitrary order only. In the literature several Blind Signal Extraction (BSE) approaches are proposed that deal with this permutation problem. In [1] and [2] sequential BSE algorithms are discussed. In these algorithms the first step is to extract one likely interesting signal. The second step is to classify the extracted signal. If the extracted signal is not the desired signal, a deflation method is used and a new, potentially interesting signal is extracted. The order in which the signals are extracted is depending on properties like sparseness, non-Gaussianity, smoothness, and linear predictability. These properties are assumed to lead to the extraction of only interesting signals because noise signals typically carry properties like G... |

26 |
Unsupervised adaptive filtering, volume 1: blind source separation’
- Haykin
- 2000
(Show Context)
Citation Context ...sing problems, e.g., the cocktail party problem. Many approaches to solve this problem use Blind Signal Processing (BSP) techniques. In BSP problems the source signals as well as the mixing system are unknown. Therefore, BSP techniques generally use no training data and no a priori knowledge about the mixing system. However, an essential problem in BSP is the permutation problem, which implies that signals can be separated or extracted in an arbitrary order only. In the literature several Blind Signal Extraction (BSE) approaches are proposed that deal with this permutation problem. In [1] and [2] sequential BSE algorithms are discussed. In these algorithms the first step is to extract one likely interesting signal. The second step is to classify the extracted signal. If the extracted signal is not the desired signal, a deflation method is used and a new, potentially interesting signal is extracted. The order in which the signals are extracted is depending on properties like sparseness, non-Gaussianity, smoothness, and linear predictability. These properties are assumed to lead to the extraction of only interesting signals because noise signals typically carry properties like Gaussiani... |

2 | Instantaneous blind signal extraction using second order statistics,”
- Bloemendal, Laar, et al.
- 2008
(Show Context)
Citation Context ...non-Gaussianity, smoothness, and linear predictability. These properties are assumed to lead to the extraction of only interesting signals because noise signals typically carry properties like Gaussianity and whiteness. An alternative BSE approach is based on Blind Source Separation (BSS) [1]–[3]. Such a method first separates all signals simultaneously with a BSS algorithm. Second, a classifier selects the desired signal from the set of separated signals. A disadvantage of both these approaches is that they are inefficient in the sense that they also separate or extract undesired signals. In [4] a novel BSE approach is introduced that randomly extracts a signal. The extraction filters are the eigenvectors Γl MC x i s1 sS ... A ... xDνD x1ν1 NF-ROS mold prior information smallest eigenvalue select w y Fig. 1: Diagram depicting the proposed BSE algorithm from the Generalized Eigenvalue Decomposition (GEVD) of correlation matrices from which the structure is composed in a very specific way. It is observed from there that each eigenvalue depends only on the mixing parameters of the signal that is extracted by the corresponding eigenvector. We believe that the approach in [4] can be exten... |

1 | Mimo instantaneous blind identification and separation based on arbitrary order temporal structure in the data,”
- Laar
- 2007
(Show Context)
Citation Context ...ne likely interesting signal. The second step is to classify the extracted signal. If the extracted signal is not the desired signal, a deflation method is used and a new, potentially interesting signal is extracted. The order in which the signals are extracted is depending on properties like sparseness, non-Gaussianity, smoothness, and linear predictability. These properties are assumed to lead to the extraction of only interesting signals because noise signals typically carry properties like Gaussianity and whiteness. An alternative BSE approach is based on Blind Source Separation (BSS) [1]–[3]. Such a method first separates all signals simultaneously with a BSS algorithm. Second, a classifier selects the desired signal from the set of separated signals. A disadvantage of both these approaches is that they are inefficient in the sense that they also separate or extract undesired signals. In [4] a novel BSE approach is introduced that randomly extracts a signal. The extraction filters are the eigenvectors Γl MC x i s1 sS ... A ... xDνD x1ν1 NF-ROS mold prior information smallest eigenvalue select w y Fig. 1: Diagram depicting the proposed BSE algorithm from the Generalized Eigenvalue... |