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A new dual subband fast NLMS adaptive filtering algorithm for blind speech quality enhancement and acoustic noise reduction

机译:一种新的双子带快NLMS自适应滤波算法,用于盲语质量增强和声噪声降低

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This paper discusses the problem of acoustic noise reduction and speech enhancement through the forward blind source separation structure. Recently we have proposed a new combination between the forward blind source separation structure and the fast normalized least mean square algorithm that provides an efficient dual algorithm for noise reduction and speech enhancement applications. In this paper we propose a new subband implementation of this recent dual algorithm, this last allows improving the speed convergence behavior of the previous proposed algorithm in its fullband form. The performance of the proposed dual subband algorithm is compared with its fullband version of the dual fast normalized least mean square algorithm and the classical fullband dual normalized least mean square algorithm, and the two channel subband forward algorithm in terms of several objective criteria. The obtained results show the good performances of the proposed dual sub-band algorithm.
机译:本文讨论了通过前向盲源分离结构的声学降噪和语音增强的问题。最近我们提出了前向盲源分离结构和快速归一化最小均方算法之间的新组合,提供了一种用于降噪和语音增强应用的有效的双重算法。在本文中,我们提出了该近期双算法的新子带实现,这最后允许提高以其完整带形式的先前提出算法的速度收敛行为。将所提出的双子带算法的性能与其全带版本的双快归一化最小均线算法和经典的全频段双标准化最小均方算法和两个信道子带前向算法的比较,以及几种客观标准。所获得的结果表明了所提出的双子频段算法的良好性能。

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