utilizing kernel adaptive filters for speech enhancement within the ale framework

Performance of the linear models, widely used within the framework of adaptive line enhancement (ale), deteriorates dramatically in the presence of non-gaussian noises. on the other hand, adaptive implementation of nonlinear models, e.g. the volterra filters, suffers from the severe problems of large number of parameters and slow convergence. nonetheless, kernel methods are emerging solutions that can tackle these problems by nonlinearly mapping the original input space to the reproducing kernel hilbert spaces. the aim of the current paper is to exploit kernel adaptive filters within the ale structure for speech signal enhancement. performance of these nonlinear algorithms is compared with that of their linear as well as nonlinear volterra counterparts, in the presence of various types of noises. simulation results show that the kernel lms algorithm, as compared to its counterparts, leads to a higher improvement in the quality of the enhanced speech. this improvement is more significant for non-gaussian noises.