A Single-Channel ICA-R Method for Speech Signal Denoising combining EMD and Wavelet

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.