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首页> 外文期刊>Journal of Computers >A Single-Channel ICA-R Method for Speech Signal Denoising combining EMD and Wavelet
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A Single-Channel ICA-R Method for Speech Signal Denoising combining EMD and Wavelet

机译：用于语音信号的单通道ICA-R方法，用于组合EMD和小波的语音信号

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—According to the problem of speech signal denoising, we propose a novel method in this paper, which combines empirical mode decomposition (EMD), wavelet threshold denoising and independent component analysis with reference (ICA-R). Because there is only one mixed recording, it is a single-channel independent component analysis (SCICA) problem in fact, which is hard to solve by traditional ICA methods. EMD is exploited to expand the single-channel received signal into several intrinsic mode functions (IMFs) in advance, therefore traditional ICA of multi-dimension becomes applicable. First, the received signal is segmented to reduce the processing delay. Secondly, wavelet thresholding is applied to the noise-dominated IMFs. Finally, fast ICA-R is introduced to extract the object speech component from the processed IMFs, whose reference signal is constructed by assembling the high-order IMFs. The simulations are carried out under different noise levels and the performance of the proposed method is compared with EMD, wavelet thresholding, EMD-wavelet and EMD-ICA approaches. Simulation results indicate that the proposed method exhibit superior denoising performance especially when signal-to-noise ratio is low, with a half shorter running time.
机译：- 根据语音信号去噪的问题，我们在本文中提出了一种新的方法，该方法将经验模式分解（EMD），小波阈值去噪和独立分量分析与参考（ICA-R）相结合。由于只有一个混合录制，因此它是一个单通道独立的分量分析（SCICA）问题，实际上是由传统的ICA方法解决的。 EMD被利用以提前将单通道接收信号扩展为多个内在模式功能（IMF），因此传统的多维ICA变得适用。首先，将接收的信号分段以降低处理延迟。其次，将小波阈值处理应用于噪声主导的IMF。最后，引入快速ICA-R以从处理的IMF中提取对象语音组件，其参考信号通过组装高阶IMF来构造。在不同的噪声水平下进行模拟，并且将所提出的方法的性能与EMD，小波阈值，EMD-小波和EMD-ICA方法进行比较。仿真结果表明，该方法尤其是当信噪比低时，尤其是较短的运行时间。

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