An Improved Technique for Speech Signal De-noising Based on Wavelet Threshold and Invasive Weed Optimization Algorithm


Speech signals play a significant role in the area of digital signal processing. When these signals pass through air as a channel of propagation, it interacts with noise. Therefore, it needs removing noise from corrupted signal without altering it. De-noising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To improve the performance of the speech which displays high power fluctuations, a new speech de-noising method based on Invasive Weed Optimization (IWO) is proposed. In addition, a theoretical model is modified to estimate the value of threshold without any priority of knowledge. This is done by implementing the IWO algorithm for kurtosis measuring of the residual noise signal to find an optimum threshold value at which the kurtosis function is maximum. It has been observed that the proposed method appeared better performance than other methods at the same condition. Moreover, the results show that the proposed IWO algorithm offered a better mean square error(MSE) than Particle Swarm Optimization Algorithm (PSO) for both one and multilevel decomposition. For instance, IWO brought an improvement in MSE in the range of 0.01 compared with PSO for multilevel decomposition.