A Parallel Architecture Using Discrete Wavelet Transform for Overcomplete ICA
基于离散小波变换的Overcomplete ICA并行结构

This paper utilizes a discrete wavelet transform to present a parallel architecture for overcomplete independ- ent component analysis (Overcomplete ICA),which is a hybrid system for consisting of two sub-overcomplete ICA processes.One process takes the high-frequency wavelet part of observations as its inputs,meanwhile the other process takes the low-frequency part.Their results are then merged to generate the final results.Compared to the existing overcomplete ICA algorithms,the proposed approach utilizes the full observation information,but the effective input length of the two parallel processed is halved.Therefore it generally provides a new way for overeomplete ICA imple- mentation.In this paper,experimental result has shown its success in extracting the in separating the mixed speech sig- nals.