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Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold

Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold. Rajeev Aggarwal , Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore , Mukesh Tiwari , Dr.Anubhuti Khare International Journal of Computer Applications (0975 – 8887)

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Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold

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  1. Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, MukeshTiwari, Dr.AnubhutiKhare International Journal of Computer Applications (0975 – 8887) Volume 20– No.5, April 2011 Presenter Chia-Cheng Chen

  2. Outline • Introduction • Multiresolution Analysis Using Filter Bank • Modifier Universal Threshold • Soft and Hard Thresholding • Results and Discussion

  3. Background

  4. Introduction • Discrete-wavelet transform (DWT) based algorithm are used for speech signal denoising. Here both hard and soft thresholdingare used for denoising. • Analysis is done on noisy speech signal corrupted by babble noise at 0dB, 5dB, 10dB and 15dB SNR levels.

  5. Multiresolution Analysis Using Filter Bank • The DWT uses Multi resolution filter banks and special wavelet filters for the analysis and reconstruction of signals. • It analysethe signal at different frequency bands with different resolutions, decompose the signal into a coarse approximation and detail information.

  6. Multiresolution Analysis Using Filter Bank • Two-level wavelet decomposition tree

  7. Multiresolution Analysis Using Filter Bank • Two-level wavelet reconstruction tree

  8. Multiresolution Analysis Using Filter Bank • Wavelet thresholding is applied to the approximation coefficient (Vn-1) and detail coefficients (Wn,Wn-1).

  9. Modifier Universal Threshold • In this paper, we removed the babble noise from noisy signal which contain the noise contents of babble noise. • We want to find threshold value that will use to remove noise from noisy signal, but also recover the original signal efficiently.

  10. Modifier Universal Threshold • Developed by Donohoand Jonstone and it is called as universal threshold • Where N denotes number of samples of noise and is standard deviation of noise.

  11. Modifier Universal Threshold • Again Universal threshold was modified with factor “k‟ in order to obtain higher quality output signal:

  12. Soft and Hard Thresholding • The soft and hard thresholding methods are used to estimate wavelet coefficients in wavelet threshold denoising. • Hard thresholding • Soft thresholding

  13. Soft and Hard Thresholding Z = (-1, 1, 100) Thr=0.4

  14. RESULTS AND DISCUSSION • We implemented babble noise removal algorithm in Matlab7.10.0 (R2010a). • Speech signal is corrupted by babble noise at 0dB, 5dB, 10dB and 15dB SNR levels.

  15. RESULTS AND DISCUSSION

  16. RESULTS AND DISCUSSION • We choose 5-level DWT and db5 wavelet. Improved threshold value is obtained by replacing threshold “thr‟ (2) with

  17. RESULTS AND DISCUSSION

  18. RESULTS AND DISCUSSION

  19. RESULTS AND DISCUSSION

  20. RESULTS AND DISCUSSION

  21. RESULTS AND DISCUSSION • Speech denoising is performed in wavelet domain by thresholding wavelet coefficients. • We found that by using modified universal threshold, we can get the better results of de-noising, especially for low level noise.

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