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By Farid Ykhlef

Speech Enhancement Based on a Combination of Spectral Subtraction and MMSE Log-STSA Estimator in Wavelet Domain. LATSI laboratory, Department of Electronic, Faculty of Engineering Sciences, University of Blida, Algeria f_ykhlef@yahoo.fr. By Farid Ykhlef. Presentation Outline (Overview).

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By Farid Ykhlef

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  1. Speech Enhancement Based on a Combination of Spectral Subtraction and MMSE Log-STSA Estimator in Wavelet Domain LATSI laboratory, Department of Electronic, Faculty of Engineering Sciences, University of Blida, Algeria f_ykhlef@yahoo.fr By Farid Ykhlef

  2. Presentation Outline (Overview) • Motivation and Goals • Spectral Weighting • Combined Spectral Subtraction and MMSE log-STSA in wavelet domain • Results • Conclusion

  3. Motivation and Goals • Mobile voice communication or speech recognition need of efficient noise reduction system. • Speech enhancement refers to the class of algorithms which aim to remove or reduce the background noise. • The noisy signal can be acquired using a single or multiple microphones. • Removing completely the background noise is practically impossible, as we do not have access to the noise signal (only the corrupted signal).

  4. Motivation and Goals • The majority of speech enhancement algorithms introduce some type of speech distortion. • Types of speech enhancement algorithms • Spectral subtractive • Wiener filtering • Statistical model based (e.g., maximum likelihood, MMSE).

  5. Spectral Weighting • The spectral weighting is usually performed in the frequency domain. • Contaminated speech by noise can be expressed as: where x(t) is the speech with noise, s(t) is the clean speech signal and n(t) is the noise process, all in the discrete time domain.

  6. Spectral Weighting • In the short-term Fourier domain: • where m is the current frame and f is the frequency index. • The actual spectral weighting is now performed by multiplying the spectrum X(m,f) with a real weighting function G(m,f) >= 0. We call G(m,f) a weighting function or weighting rule.

  7. Weighting rule Noise Estimation Spectral Weighting • The result is then, • the spectral weighting attempts to estimate s(t) from x(t). IDFT + Overlap-add × Windowing + DFT

  8. Spectral Weighting • Since n(t) is a random process, certain approximations and assumptions must be made. • The noise is (within the time duration of speech segments) a short-time stationary process. • Noise is assumed to be uncorrelated to the speech signal. • The noise is estimated from pauses in the speech signal using a VAD technique with this formula: is the spectrum of the noisy speech is the forgetting factor.

  9. Spectral Weighting • The Spectral Subtraction S.F. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 27, April 1979, pp. 113-120. Written as a weighting rule • undesirable distortions : ”musical noise”

  10. Spectral Weighting • MMSE log-STSA Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean -square error log-spectral amplitude estimator,” IEEE Trans. on ASSP, 1985, pp. 443-445. • The MMSE log-STSA estimator minimizes the mean squared error of the logarithmic spectra of the original undisturbed speech signal and the processed output signal.

  11. Spectral Weighting • The weighting function in this caseis where represents the function: and represent the modified Bessel functions of zero and first order.

  12. Combined Spectral Subtraction and MMSE log-STSA estimator in Wavelet Domain • Discrete Wavelet Transform • DWT can be simply thought of in terms of filter banks. approximation coefficients DWT IDWT cA h' ↑2 h ↓2 Original signal Original reconstructed cD ↓2 g' g ↑2 detail coefficients Decomposition and reconstitution Algorithm h = low-pass decomposition filter; g = high-pass decomposition filter; ↓2 = down-sampling operation. h’ = low pass reconstruction filter; g’ = high-pass reconstruction filter; ↑2 = up-sampling operation

  13. Combined Spectral Subtraction and MMSE log-STSA estimator in Wavelet Domain Hybrid System cleanedapproximation coefficients approximation coefficients Spectral Subtraction cAc cA Cleaned speech Noisy speech DWT IDWT MMSE Log-STSA cDc cD detail coefficients cleaneddetail coefficients

  14. Results Table (SNR/SNRseg)out(dB)

  15. Results Noisy Speech time evolutions and spectrograms

  16. Results Spectral Subtraction time evolutions and spectrograms

  17. Results MMSE log-STSA time evolutions and spectrograms

  18. Results Hybrid System time evolutions and spectrograms

  19. Summary • To explore the advantages of spectral subtraction and MMSE log-STSA methods, in this work a new scheme based on their combination in wavelet domain was proposed for noise reduction fields. • A comparative study between with other known methods was carried out to evaluate the performance of the proposed system. • The experimental results show that our proposed hybrid system is capable of reducing noise and is an adequate procedure to improving the quality of the speech enhancement application.

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