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An Efficient Multichannel Linear Prediction-Based Blind Equalization Algorithm in Near Common Zeros Condition
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DOI：10.1109/LSP.2014.2301831.png)
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Abstract
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This letter proposes an efficient multichannel acoustic channel equalization method under insufficient channel diversity conditions. To overcome an ill-posed problem caused by near common zeros (NCZs) conditions between different channels, a regularization method that restricts the filter norm has been investigated. However, direct application of this method to the linear-predictive multi-input equalization (LIME) method is not effective. To address this situation, this letter puts forth a novel method to disregard the erroneous term of the LIME solution matrix and to increase forced channel diversity (FCD). The accuracy of the proposed equalization filter is compared to that of the conventional regularization method. Experimental results confirm that the NCZs problem can be solved by adopting the proposed methods.
Keywords:
Channel diversity
linear-predictive multi-input equalization
near common zeros
speech dereverberation
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