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.
Adaptive frequency hopping (AFH) is considered to be it iiiorr efft'ctive iiiethod Cor combating active jonuning in military radio systenis than normal frequency hopping (FH). This paper presents the iiovsl conqt-co~ivergait h i e of AFH system and defuies it as 81' 1 important performance index of the AFH systeiti. According to the tlueshold-based way, we deduce how to coiiipute the ronvergait time, and draw out some useful conclusions. In tlie lend, the si~i~ulatioiw results indicate the validity of oiir analysis.
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