Study On The Speech Enhancement Method Of The Multiple Speech Signals Separation

Speech is very important for information transmission and communication of human. The quality of speech is not only influence the human hearing but also the other steps in speech processing system. For the interference pollution in real world, speech quality always become bad. Speech enhancement is based on removing the interference in speech to recover the original pure speech. Different method could be designed to eliminate different kinds of interference. The multiple speech separation technology to remove the speech interference is one of the focus in the field of speech enhancement.This paper study on the separation method of multiple speech signals and mainly include three aspects as follows:(1) The blind source separation method of speech signal based on independent component analysis. When the number of observed signals which is linear mixed by sources is not less than the number of sources, the method of independent component analysis could resolve the blind source separation problem effectively and eliminate the noise of speech interference well. The key of independent component analysis method is to calculate the demixing matrix. Then sources could be recovered from the observed signals by multiplying the demixing matrix with the observed signal vector. This paper focus on the fast independent component analysis algorithm, and studied two more efficient version of the algorithm to improve the accuracy of the demixing matrix and the quality of recovered sources.(2) The underdetermined blind identification method based on clustering. When the number of observed signals is less than the number of sources, called as the underdetermined condition, the independent component analysis method is not suitable for the separation any more. It is necessary to utilize the sparse of signals to evaluate the mixing matrix for further separation. This paper studied the underdetermined blind identification algorithm based on clustering, and give a method by using the Iterative self organizing data analysis techniques algorithm to obtain the mixing matrix. Then, a pre-proceeding step and a post-proceeding step are proposed to improve the robust of the algorithm and the accuracy of the evaluated mixing matrix.(3) The underdetermined speech separation by a step-wise method. In the underdetermined condition, methods based on statistics are always adopted for the separation by using the sparse of signals. However, there are still some mutual interference and music noise in the separated sources, because speech sources do not satisfy the W-disjoint orthogonality condition strictly in time frequency domain which led to the overlapping in some extent between sources. this paper utilize the mixing matrix that get from some clustering method, and cancel the sources one by one from each mixed signal to produce corresponding zero value points in the mixed signals. Then, construct multiple binary mask from zero points to extract the disjoint or overlapped sources from the mixture and separate them step by step. The interference and music noise of the separated signals is depressed in some extent by this method, and the quality of the separation become better.Experimental result reveal the efficiency of the methods above in this paper.