Research on speech enhancement based on nonnegative matrix factorization and improved genetic algorithm

In order to improve the speech enhancement effect and overcome the weakness of falling into the local optimum of the traditional nonnegative matrix factorization, this paper presents speech enhancement methodology based on nonnegative matrix factorization combined with the improved genetic algorithm. The methodology consists of training stage and enhancement stage. In the training stage, instead of gradient descent, iterative learning is achieved by selecting several directions in super flat close to deflector cone as the mutation. By adding the mutation operator and simulated annealing operator to NMF, the proposed algorithm is used to acquire the accurate noise dictionary as prior information. In the de-noising stage, NMF-IGA is used to evaluate the dictionary matrix and the activation matrix of the de-noised speech. Then using the dictionary of de-noised speech to get the enhanced speech spectrogram and reconstruct the enhanced speech. It is verified that the proposed algorithm yields less residual noise and better speech quality than traditional nonnegative matrix factorization and Bayes nonnegative matrix factorization algorithm.