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Optimal Selection of Wavelet and Threshold Using Cuckoo Search for Noise Suppression in Speech Signals


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DOI: https://doi.org/10.15866/irecos.v9i9.372

Abstract


Application of threshold based techniques to wavelet transformed speech signals can act as the noise suppression algorithm. There are types of wavelets and thresholding algorithms which exist and selection of optimal wavelet and optimal thresholding technique based on the input speech signal is vital for having better noise suppression. Selection of optimal number of decomposition levels is also has an impact on the resultant signal. In this paper, optimal selection of wavelet, level and thresholding technique for noise suppression in speech signals using Cuckoo search is proposed. After finding these, the signal is wavelet transformed andis applied proposed adaptive coefficient process which is then done thresholding. Subsequently, reconstruction is carried out to have the noise suppressed signal. The implementation is carried out with MATLAB and the evaluation metrics employed are Itakura–Saito distance (IS) and MSE.The results are taken under various noise conditions and compared with existing technique. From the results obtained, IS and MSE values for proposed is far lower than existing technique.Total IS average for proposed was about 0.78×105 compared with 1.3×105 that of existing. Total MSE average for proposed was about 0.22×10-3 compared with 1.25×10-3 that of existing. The results show the effectiveness of the proposed technique.
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Keywords


Noise Suppression; DWT Transform; Wavelets; Thresholding Techniques; Optimization; Cuckoo Search

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References


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