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

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

Introduction  Concepts: Speech enhancement

Introduction  Block processing

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

Methodology  Spectrum subtraction  Wiener filtering  Kalman filtering

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

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

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

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

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

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

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

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,

Experiments & Results  Experiment Setup  Comparison of enhancement effects

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

Enhancement in White Noise

Enhancement in Colored Noise

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

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