Non-negative matrix factorization of mixed speech signals based on improved particle swarm optimization

Abstract

NMF (non-negative matrix factorization) is a recently addressed speech signal processing method, In this paper, we proposed a new NMF algorithm based on improved PSO (particle swarm optimization) techniques at aims to extract non-negative components with low cross-talking error and high SNR. Compared with standard PSO algorithm, the improved PSO can overcome lower velocity of convergence by updating dynamic inertia weight. Our discussion is supported by experimental results for separating speech signals, which show that the proposed approach exhibits good performance than traditional NMF methods.