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Underdetermined Mixing Matrix Estimation in Wavelet Packet Domain Using LMM by Adaptive EM-type Algorithm and Comparisons with Different Wavelets

机译:自适应EM型算法的LMM不确定小波包域混合矩阵估计及与不同小波的比较

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摘要

Speech process has benefited a great deal from the wavelet transforms. Wavelet packets decompose signals in to broader components using linear spectral bisecting. In this paper, mixtures of speech signals are decomposed using wavelet packets, the phase difference between the two mixtures are investigated in wavelet domain. In our method Laplacian Mixture Model (LMM) is defined. An Expectation Maximization (EM) algorithm is used for training of the model and calculation of model parameters which is the mixture matrix. And then we compare estimation of mixing matrix by LMM-EM with different wavelet. Therefore individual speech components of speech mixtures are separated by using estimated matrix.
机译:语音处理从小波变换中受益匪浅。小波包使用线性频谱二等分将信号分解为更宽的分量。本文利用小波包对语音混合信号进行分解,并在小波域内研究了两种混合信号之间的相位差。在我们的方法中,定义了拉普拉斯混合模型(LMM)。期望最大化(EM)算法用于训练模型和计算作为混合矩阵的模型参数。然后,我们比较了不同小波的LMM-EM估计的混合矩阵。因此,通过使用估计的矩阵来分离语音混合物的各个语音成分。

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