In this paper, a new technique to solve the nonlinear blind source separation problem (NBSS) is introduced. The method is based on the concept of reducing the high frequency component of the nonlinear mixed signal by dividing the mixed signal into blocks in the time domain, with any arbitrary size. To remove the distortion of the nonlinear function, the discreet cosine transform (DCT) is applied on each block. By adaptively adjusting the size of the DCT block of data, the highly correlated subblocks, can be estimated, then the correlation between the highly correlated sub-blocks can be reduced. To complete the separation process, the linear blind source separation (BSS) algorithm based on the wavelet transform is used to reduced the correlation between the highly correlated DCT subblock. Performed computer simulations have shown the effectiveness of the idea, even in presence of strong nonlinearities and synthetic mixture of real world data (like speech and image signals).