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

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 case is 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. h g ↓2 Original signal h' g' ↑2 Original reconstructed DWTIDWT cA cD 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 approximation coefficients detail coefficients

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

14. Results Table (SNR/SNRseg)out (dB) SNRinput (dB) Spectral Subtraction MMSE log-STSA Hybrid System 1011.22/1.608.54/0.4011.20/1.56 510.30/ 0.757.57/-0.4410.20/0.61 08.23/-0.545.81/-1.617.08/-1.22

15. Results time evolutions and spectrograms Noisy Speech

16. Results time evolutions and spectrograms Spectral Subtraction

17. Results time evolutions and spectrograms MMSE log-STSA

18. Results time evolutions and spectrograms Hybrid System

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.