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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >PARTICLE SWARM OPTIMIZATION FOR OPTIMIZING LEARNING PARAMETERS OF FUNCTION FITTING ARTIFICIAL NEURAL NETWORK FOR SPEECH SIGNAL ENHANCEMENT
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PARTICLE SWARM OPTIMIZATION FOR OPTIMIZING LEARNING PARAMETERS OF FUNCTION FITTING ARTIFICIAL NEURAL NETWORK FOR SPEECH SIGNAL ENHANCEMENT

机译：粒子群优化算法在语音信号增强中的功能拟合人工神经网络学习参数优化

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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.
机译：语音信号受到各种干扰源产生的噪声的影响。从语音信号中去除噪声可以被视为信号处理中的活跃研究领域。因此，我们需要在这方面有力的方法。因此，本文使用功能拟合（FitNet）人工神经网络模型来增强语音信号。在FitNet学习过程中使用了粒子群优化（PSO）来优化FitNet学习参数（例如学习率，动量变量和网络权重），以获得语音信号增强的最佳结果。同时，在这项工作中提出了用于优化学习参数值的不同优化技术。这样做是为了提高FitNet模型用于信号增强的性能。当采用FitNet的PSO（群大小等于40，PSO迭代次数等于100）时，通过实验获得了更好的结果（320个学习步骤，PSNR等于38，均方误差（MSE）等于0.0027）。良好的结果（312个学习步骤，PSNR当采用建议的优化技术（学习率等于0.00003，使用Levenberg-Marquardt（LM）作为学习算法在一个隐藏层中包含5个隐藏单元）优化学习参数时，也获得了等于35.94和MSE等于0.00002）。

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