期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：11
出版社：Journal of Theoretical and Applied
摘要：Speech signals are effected by noise generated by various sources of interferences. Removing noise from speech signals can be regarded as an active research area in signal processing. Thus, we need powerful methods in this area. Therefore, Function Fitting (FitNet) Artificial Neural Networks model was used in this paper for enhancing speech signals. Particle Swarm Optimization (PSO) was used during FitNet learning process to optimize the FitNet learning parameters (such as learning rate, momentum variable and network weights) to achieve best results of speech signal enhancement. At the same time, different optimization techniques for optimizing the values of learning parameters were suggested in this work. This is done to improve the performance of FitNet model for signal enhancement. Better results (320 learning steps, PSNR equal 38 and mean square error (MSE) equal 0.0027) from experiments were achieved when adopting PSO with FitNet with swarm size equal 40 and PSO number of iterations equal 100. Good results (312 learning steps, PSNR equal 35.94 and MSE equal 0.00002) were obtained also when adopting the suggested optimization techniques (learning rate equal 0.00003, 5 hidden units in one hidden layer with the using of Levenberg-Marquardt (LM) as learning algorithm) for optimizing the learning parameters.
