Single-Channel Blind Separation Using Adaptive Mode Separation- Based Wavelet Transform and ICA Single-Channel Separation of the

Abstract

In this paper, a new method to solve the signal-channel blind source separation (SCBSS) problem has been proposed. The method is based on combining the Adaptive Mode Separation-Based Wavelet Transform (AMSWT) and the ICA-based single channel separation. First, the amplitude spectrum of the instantaneous mixture signal is obtained via the Fourier transform. Then, the AMSWT is introduced to adaptively extract spectral intrinsic components (SIC) by applying the variational scaling and wavelet functions. The AMSWT is applied to every mode to obtain the time-frequency distribution. Then the timefrequency distribution of the mixed signal is exploited. The ICA-based single-channel separation has been applied on spectral rows corresponding to different time intervals. Finally, these components are grouped using the β-distance of Gaussian distribution Dβ. Objective measure of separation quality has been performed using the scale-invariant (SI) parameters and compared with the existing method to solve SCBSS problem. Experimental results show that the proposed method has better separation performance than the existed methods, and the proposed method present a powerful method to solve de SCBSS problem. Keywords: Signal-channel blind source separation. Adaptive Mode Separation-Based Wavelet Transform. Spectral decomposition-based method. β-distance of Gaussian distribution