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Semi-blind source separation (SBSS) is a special case of the well-known source separation problem when some partial knowledge of the source signals is available to the system. In particular, a batch-wise adaptation in the frequency domain based on the independent component analysis (ICA) can be effectively used to jointly perform source separation and multi-channel acoustic echo cancellation (MCAEC)...
This paper examines the technique of using a memoryless noise-suppressing nonlinearity in the adaptive filter error feedback-loop of an acoustic echo canceler (AEC) based on normalized least-mean square (NLMS) when there is an additive noise at the near-end. It will be shown that introducing the nonlinearity to ldquoenhancerdquo the filter estimation error is well-founded in the information-theoretic...
In this paper, we propose a new method based on a coherent source spectral estimation for solving the permutation problem of frequency-domain blind source separation (BSS). It combines the robustness of the State Coherence Transform (SCT) to recursively estimate a smooth phase spectrum associated with each source and the precision of the inter-frequency correlation to solve for a correct permutation...
This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic text categorization problems: the interpretability and the performance. Specifically, to solve the interpretability issue, the latent semantic analysis technique is used to construct a topic space, in which each dimension...
This paper examines the technique of using a noise suppressing nonlinearity in the adaptive filter error feedback loop of the acoustic echo canceler (AEC) based on the least mean square (LMS) algorithm when there are both double-talk and white background noise at the near-end. By combining the previously introduced noise suppressing technique with a compressive nonlinearity derived from the theory...
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