2. Single-Channel Speech Enhancement in Both White and Colored Noise Xin Lei Xiao Li Han Yan June 5, 2002

3. Outline  Introduction  Methodology Spectrum subtraction Wiener filtering Kalman filtering  Experiments & Results  Conclusion

4. Introduction  Concepts: Speech enhancement

5. Introduction  Block processing

6. Introduction  Applications Voice communication system Speech recognition system  Keywords White noise Colored noise

7. Methodology  Spectrum subtraction  Wiener filtering  Kalman filtering

8. Method I: Spectrum Subtraction  Assume we know the psd of noise

9. Diagram for Spectrum Subtraction FFT Noisy speech y(n) Phase IFFT Subtraction of Enhanced speech x(n)

10. Method II: Wiener Filtering Wiener Filter H(w) Cross Power Spectral Density: Signal and Noise are independent:

11. Wiener Filtering (cont’d) If, Wiener filter minimizes mean square error: H(w) weights spectrum according to different frequencies:

12. Wiener Filter Diagram estimate Noisy speech y(n) Enhancement speech x(n) estimate Wiener Filter

13. Method III: Kalman Filtering  AR model of speech Prediction Observation  Goal: MMSE estimation  Basic idea: Estimation = Prediction + Gain (Observation - Prediction)

14. Parameter Estimation  Need to know  Assume prior knowledge of  Estimate speech parameters using Yule- Walker Equation. To achieve MMSE

15. Kalman Filtering of Colored Noise  Colored noise has the same state-space AR model as speech does.  Assume prior knowledge of noise model parameters  Estimate speech model parameters using Yule- Walker Equation. To achieve MMSE,

16. Experiments & Results  Experiment Setup  Comparison of enhancement effects

17. Experiments Setup  Clean speech  Noise simulation  SNR: 0dB

18. Enhancement in White Noise

20. Enhancement in Colored Noise

21. Conclusion  Comparisons Three different methods of speech enhancement Nonparametric vs. parametric estimation White noise vs. colored noise  Future work Speech/noise detector Iterative algorithm for Wiener/Kalman filters Recursive algorithm to reduce computation

22. Thank You !