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Noisy Blind Source Signal Separation Based on Nonlinear Autocorrelation Using LMS Algorithms In DWT Domain
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Abstract: (18138 Views) |
| Blind source separation is the technique that anyone can separate the original signals from their mixtures without any knowledge about the mixing process, but using some statistical properties of original source signals. Independent component analysis is a statistical method expressed as a set of multidimensional observations that are combinations of unknown variables which are assumed to be statistically independent with respect to each other. In this paper we will use the nonlinear autcorrelation function as an object function to separate the source signals from the noisy mixing signals. Also we apply the wavelet transform in our proposed algorithm. Maximization of the object function in wavelet domain using the LMS algorithm will be obtained the coefficients of a linear filter which separate the source signals with high SNR. To calculate the performance of the proposed algorithm, two parameters of Performance Index and Signal to Noise and Interference Ratio will be used. To test the proposed algorithm, we will use Inovation Gaussian signals, Speech signals and ECG signals. Finally level of wavelet decomposition effects will be consider on the obtained results. It will be shown that the proposed algorithm gives better results than the other methods such as NoisyNA method that has been proposed by Shi. |
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| Keywords: BSS, ICA, Nonlinear Autocorrelation Function, LMS Algorithm, Speech Signal Processing, Electrocardiogram Signals, Discrete Wavelet Transform, NoisyNA |
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Full-Text [PDF 758 kb]
(2433 Downloads)
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Type of Study: Research |
Received: 2013/05/25 | Accepted: 2014/01/25 | Published: 2014/06/16
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