Enhancement of Speech Signal by Adaptation ofScales and Thresholds of Bionic Wavelet Transform Coefficients

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

article

Rupali Sharma¹  Preety D Swami² 

[1] Department of Electronics & Communication, Samrat Ashok Technological Institute;Department of Electronics & Instrumentation, Samrat Ashok Technological Institute

关键词: Adaptive thresholding;    Additive White Gaussian Noise;    Bionic Wavelet Transform;    Continuous Wavelet Transform;    Speech enhancement.;   

来源: Research & Reviews

【 摘 要 】

This paper proposes a speech signal enhancement method in which the wavelet transform scales and thresholds both are adaptive depending on the input noisy signal affected by Additive White Gaussian Noise (AWGN). The proposed Estimated Noise and Adaptive Threshold Bionic Wavelet Transform (ENAT-BWT) method analyses the incoming noisy speech signal at 22 scales, from 7 to 28, of the BWT for negative SNR levels and at 28 scales, from 6 to 33, of the BWT for positive SNR levels. Initially, the thresholds for various noise levels are determined manually that provide the best signal to noise ratios (SNR). Then, using curve fitting approach a generalized model is obtained that provides the best threshold parameter for input noisy signal of any noise standard deviation. Thus the algorithm selects the threshold value from the generalized model and soft thresholding is applied to the BWT coefficients. Finally, inverse bionic wavelet transform (IBWT) of thresholded BWT coefficients is computed which provides the enhanced speech signal. Results are measured using signal-to-noise ratio (SNR) and segmental signal-to-noise ratio (SSNR) for additive white Gaussian noise at various input SNR levels. Results are compared with variety of speech enhancement techniques, including BWT, PWT and Ephraim Malah filtering. Overall results indicate that SNR and SSNR improvements for the proposed approach are far superior than those of the techniques under comparison.

【 授权许可】

Unknown