Robustness Techniques for Feature Extraction - Berlin Chen

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Spectral Subtraction (SS) • Modification: Nonlinear Spectral Subtraction (NSS) Pˆ P S ⎧PX ( ω ) − PN ( ω ), if PX ( ω ) ≥ PN ( ω ) ( ω ) = ⎨ ⎩PN ( ω ), otherwise ( ω ) and P ( ω ): smoothed noisy and noise spectrum X N or Pˆ P φ ⎧P ( ) ( ) ( ) ( ) ( ) X ω − φ ω , if PX ω > φ ω + β ⋅ PN ω ( ) S ω = ⎨ ⎩β ⋅ P ( ω ) N , otherwise ( ω ) and P ( ω ) X N : smoothed noisy and noise ( ω ): a non - linear function according to SNR spectrum 16
Spectral Subtraction (SS) • Spectral Subtraction can be viewed as a filtering operation Pˆ S ( ω ) = P ( ) ( ) X ω − PN ω ⎡ P ( ) ( ) N ω = PX ω ⎢1 − P ( ω ) Power Spectrum P ( ) S ω ( ω ) + P ( ω ) ⎤ ⎡ ⎤ ⎥ = P ( ) X ω ⎢ ⎥ (supposed that PX S + ⎣ X ⎦ ⎣ PS N ⎦ −1 ⎡ 1 ⎤ P ( ) ( ) ( ) N ω = PX ω ⎢1 + ( R = :instantaneous SNR ) R( ) ⎥ ω ⎣ ω ⎦ P ( ) S ω ∴The time varyingsuppression filter is given approximately by : ( ω) ≈ P ( ω) P ( ω) N ) H ( ω) ⎢ ⎡ = 1 + ⎣ 1 R ( ω) ⎤ ⎥ ⎦ −1 / 2 Spectrum Domain 17
Page 1 and 2: Robustness Techniques for Speech Re
Page 3 and 4: Introduction • The Diagram for Sp
Page 5 and 6: The Noise Types s[m] h[m] n[m] x[m]
Page 7 and 8: Additive Noises 7
Page 9 and 10: Noise Characteristics • White Noi
Page 11 and 12: Robustness Approaches
Page 13 and 14: Three Basic Categories of Approache
Page 15: Spectral Subtraction (SS) • Probl
Page 19 and 20: Wiener Filtering • Minimize the e
Page 21 and 22: Wiener Filtering • The time varyi
Page 23 and 24: • Approach - II : Wiener Filterin
Page 25 and 26: Wiener Filtering Clean Speech Noisy
Page 27 and 28: Cepstral Mean Normalization (CMN)
Page 33 and 34: RASTA Temporal Filter • The IIR t
Page 35 and 36: Retraining on Corrupted Speech •
Page 37 and 38: Signal Decomposition Using HMMs •
Page 39 and 40: Parallel Model Combination (PMC)
Page 41 and 42: Parallel Model Combination (PMC)
Page 43 and 44: Vector Taylor Series (VTS) P. J. Mo
Page 45 and 46: Vector Taylor Series (VTS) • Appl
Page 47 and 48: Retraining on Compensated Features
Page 49 and 50: Principal Component Analysis 49
Page 51 and 52: PCA Applied in Inherently Robust Fe
Page 57 and 58: Linear Discriminative Analysis •
Page 59 and 60: Minimum Classification Error • Mi
Page 61 and 62: Minimum Classification Error • Mi

Robustness Techniques for Feature Extraction - Berlin Chen

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