An improved technique for speech signal de-noising based on wavelet threshold and invasive weed optimization algorithm
Joint Authors
Abd, Haydar Jabbar
Salim, Nurah
Shaban, Ali
Source
Journal of University of Babylon for Engineering Sciences
Issue
Vol. 25, Issue 4 (31 Aug. 2017), pp.1413-1423, 11 p.
Publisher
Publication Date
2017-08-31
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Shaban, Ali& Abd, Haydar Jabbar& Salim, Nurah. 2017. An improved technique for speech signal de-noising based on wavelet threshold and invasive weed optimization algorithm. Journal of University of Babylon for Engineering Sciences،Vol. 25, no. 4, pp.1413-1423.
https://search.emarefa.net/detail/BIM-923492
Modern Language Association (MLA)
Shaban, Ali…[et al.]. An improved technique for speech signal de-noising based on wavelet threshold and invasive weed optimization algorithm. Journal of University of Babylon for Engineering Sciences Vol. 25, no. 4 (2017), pp.1413-1423.
https://search.emarefa.net/detail/BIM-923492
American Medical Association (AMA)
Shaban, Ali& Abd, Haydar Jabbar& Salim, Nurah. An improved technique for speech signal de-noising based on wavelet threshold and invasive weed optimization algorithm. Journal of University of Babylon for Engineering Sciences. 2017. Vol. 25, no. 4, pp.1413-1423.
https://search.emarefa.net/detail/BIM-923492
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in English and Arabic.
Record ID
BIM-923492