The observed angular distribution is also clustered, with uniformly distributed clusters, and arrivals within clusters that have a Laplacian distribution. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering by Barry D.
Eng , Abstract—A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites.
The weights are chosen to minimize the filter output power subject Abstract - Cited by 4 self - Add to MetaCart Abstract—A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed.
The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map.
The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models.
The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach. This paper presents a robust and precise method for solving the permutation problem of frequency-domain blind source separation.
It is based on two previous approaches: the direction of arrival estimation and the inter-frequency correlation. We discuss the advantages and disadvantages of the two app Abstract - Cited by 31 self - Add to MetaCart This paper presents a robust and precise method for solving the permutation problem of frequency-domain blind source separation.
We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit their respective advantages. We also present a closed form formula to estimate the directions of source signals from a separating matrix obtained by ICA. Experimental results show that our method solved permutation problems almost perfectly for a situation that two sources were mixed in a room whose reverberation time was ms. On ideal binary mask as the computational goal of auditory scene analysis by Deliang Wang - in Speech Separation by Humans and Machines , In a natural environment, a target sound, such as speech, is usually mixed with acoustic interference.
A sound separation system that removes or attenuates acoustic interference has many important applications, such as automatic speech recognition ASR and speaker identification in real Abstract - Cited by 99 40 self - Add to MetaCart In a natural environment, a target sound, such as speech, is usually mixed with acoustic interference. A sound separation system that removes or attenuates acoustic interference has many important applications, such as automatic speech recognition ASR and speaker identification in real.
Signal Processing. Abstract—We consider a sensor array located in an enclo-sure, where arbitrary transfer functions TFs relate the source signal and the sensors. The array is used for enhancing a signal contaminated by interference.
Constrained minimum power adaptive beamforming, which has been suggested by Frost an Abstract - Cited by 77 10 self - Add to MetaCart Abstract—We consider a sensor array located in an enclo-sure, where arbitrary transfer functions TFs relate the source signal and the sensors.
Constrained minimum power adaptive beamforming, which has been suggested by Frost and, in particular, the generalized sidelobe canceler GSC version, which has been developed by Griffiths and Jim, are the most widely used beamforming techniques.
These methods rely on the assumption that the received signals are simple delayed versions of the source signal. The good interference suppression attained under this assumption is severely impaired in complicated acoustic environments, where arbitrary TFs may be encountered. In this paper, we consider the arbitrary TF case. We derive a suboptimal algorithm that can be implemented by estimating the TFs ratios, instead of estimating the TFs.
The TF ratios are estimated by exploiting the nonstationarity characteristics of the desired signal. The algorithm is applied to the problem of speech enhancement in a reverberating room.
The discussion is supported by an experimental study using speech and noise signals recorded in an actual room acoustics environment. Index Terms—Beamforming, nonstationarity, speech enhance-ment. In this paper a Generalised Singular Value Decomposition GSVD based algorithm is proposed for enhancing multi-microphone speech signals degraded by additive coloured noise.
This GSVD-based multi-microphone speech signal Chen, L. Yip, J. Elson, H. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included.
Several beamformer implementations are briefly described. View on IEEE. Save to Library Save. Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures, Tables, and Topics from this paper. Beamforming Signal processing Nomenclature notation. Citation Type. Has PDF. Publication Type. More Filters. A novel approach to adaptive beamforming for multi-path broadband signals. View 1 excerpt, cites methods. Signal-Independent Array Processing.
Orthogonal lattice algorithms for adaptive filtering and beamforming. Computer Science, Mathematics. A robust algorithm for linearly constrained adaptive beamforming. Mathematics, Computer Science. View 1 excerpt, cites background. Path uncertainty robust beamforming. Introduction to Array Processing.
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