ICA-Based MAP Algorithm for Speech Signal Enhancement

This paper proposes a novel MAP denoising algorithm that uses the ICA transformation and provide a derivation demonstrating the type of situations in which the use of ICA transformation is expected to achieve best results. We also propose an employment of generalized Gaussian model (GGM) for modelling the speech and noise distributions. The performance of the proposed speech enhancement algorithm is compared with the Wiener filtering and sparse code shrinkage method. The experiments are focused on speech signal corrupted by a non-Gaussian noise. The experimental results show that the proposed algorithm achieves significantly better performance than both the Wiener filtering and the sparse code shrinkage method.