The Experts below are selected from a list of 29772 Experts worldwide ranked by ideXlab platform
Soren Holdt Jensen - One of the best experts on this subject based on the ideXlab platform.
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an amplitude spectral capon estimator with a variable Filter Length
European Signal Processing Conference, 2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
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EUSIPCO - An amplitude spectral Capon estimator with a variable Filter Length
2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
Jesper Kjaer Nielsen - One of the best experts on this subject based on the ideXlab platform.
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an amplitude spectral capon estimator with a variable Filter Length
European Signal Processing Conference, 2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
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EUSIPCO - An amplitude spectral Capon estimator with a variable Filter Length
2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
Paris Smaragdis - One of the best experts on this subject based on the ideXlab platform.
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an amplitude spectral capon estimator with a variable Filter Length
European Signal Processing Conference, 2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
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EUSIPCO - An amplitude spectral Capon estimator with a variable Filter Length
2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
Mads Graesboll Christensen - One of the best experts on this subject based on the ideXlab platform.
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an amplitude spectral capon estimator with a variable Filter Length
European Signal Processing Conference, 2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
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EUSIPCO - An amplitude spectral Capon estimator with a variable Filter Length
2012Co-Authors: Jesper Kjaer Nielsen, Paris Smaragdis, Mads Graesboll Christensen, Soren Holdt JensenAbstract:The Filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the Length of the Filters in these Filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the Filter Length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal Filter Length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
Shoji Makino - One of the best experts on this subject based on the ideXlab platform.
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blind source separation with different sensor spacing and Filter Length for each frequency range
IEEE Workshop on Neural Networks for Signal Processing, 2002Co-Authors: Hiroshi Sawada, Shoko Araki, Ryo Mukai, Shoji MakinoAbstract:This paper presents a method for blind source separation using several separating subsystems whose sensor spacing and Filter Length can be configured individually. Each subsystem is responsible for source separation of an allocated frequency range. With this mechanism, we can use appropriate sensor spacing as well as Filter Length for each frequency range. We obtained better separation performance than with the conventional method by using a wide sensor spacing and a long Filter for a low frequency range, and a narrow sensor spacing and a short Filter for a high frequency range.
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NNSP - Blind source separation with different sensor spacing and Filter Length for each frequency range
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2002Co-Authors: Hiroshi Sawada, Shoko Araki, Ryo Mukai, Shoji MakinoAbstract:This paper presents a method for blind source separation using several separating subsystems whose sensor spacing and Filter Length can be configured individually. Each subsystem is responsible for source separation of an allocated frequency range. With this mechanism, we can use appropriate sensor spacing as well as Filter Length for each frequency range. We obtained better separation performance than with the conventional method by using a wide sensor spacing and a long Filter for a low frequency range, and a narrow sensor spacing and a short Filter for a high frequency range.
