Speech enhancement using non-negative matrix factorization solved by improved alternating direction method of multipliers

For the task of monaural speech enhancement, A version of Sparse Nonnegative matrix factorization (Sparse NMF) using improved Alternating Direction Method of Multipliers (IADMM) with generalized Kullback-Leibler divergence is proposed. In this paper, an alternating direction method of multipliers (ADMM) for NMF is studied, which deals with the NMF problem using the cost function of beta divergence. Our study shows that this algorithm outperforms state-of-the-art algorithms on synthetic data sets, but the study shows that it presents unstable behavior and low accuracy on real data sets. Therefore, we propose an improved algorithm for sparse NMF to solve this problem. The algorithm minimizes the K-L divergence with a pivot element weighting iterative (PEWI) method. Experimental results demonstrated that the proposed algorithm is more stable and accurate and also obtained better performance than the state-of-the-art speech enhancement algorithms.