Blind source separation by combining indepandent component analysis with complex discrete wavelet transform

It is well known that independent component analysis (ICA) is a useful method for blind source separation although it does have some drawbacks, such as performing poorly on unsteady sounds. In this study, in order to improve this deficiency, a new method combining ICA with the complex discrete wavelet transform is proposed and verification of source separation with relation to the problems of permutation and scaling in the ICA are performed. Through comparison of the results according to the signal noise ratio (SNR), the effectiveness of the proposed method is confirmed.