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Speech Enhancement

Speech Enhancement. Using a Minimum Mean Square Error Short-Time Spectral Amplitude Estimation method. Process Flow. Segmenting of Signal. The sample is divided into frames whose length is equal to 25ms with a shift percentage of 40% or 10ms.

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Speech Enhancement

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  1. Speech Enhancement Using a Minimum Mean Square Error Short-Time Spectral Amplitude Estimation method

  2. Process Flow

  3. Segmenting of Signal • The sample is divided into frames whose length is equal to 25ms with a shift percentage of 40% or 10ms. • The Window Length is equal to the 25ms times the Sampling Frequency. • Example • Sampling Frequency is equal to 8000 samples/s • Window Length = 0.025s * 8000 samples/s = 200 samples • Each frame is then windowed using a Hamming window.

  4. Initial Silence Segments • The initial silence or speech inactivity period is assumed to be 250ms. • This is to allow for a sufficient amount of data to be analyzed for the Noise Spectrum prior to attempting Voice Activity Detection (VAD). • The Number of Initial Silence Segments (NISS) = (Initial Silence * Sampling Frequency - Window Length)/(Shift Percentage* Window Length). • Example • Using our previous values. • NISS = (0.25s * 8000 samples/s - 200 samples)/0.4*200 samples = 22.5. • The value is rounded down to the nearest whole number

  5. Phase Calculation using FFT • The Fast Fourier Transform of each frame is calculated. • The phase component of the FFT is calculated for use in reconstruction of the enhanced signal.

  6. Noise Power Spectrum • An initial Noise Power Spectrum and the Noise Power Spectrum Variance (λd) is calculated using the mean values of the FFT for the NISS. • For each frame in the NISS, the Noise Power Spectrum and the Noise Power Spectrum Variance are updated. • The frames after the NISS are evaluated using a Voice Activity Detector (VAD) which utilizes the Noise Power Spectrum. • If the frames are determined to contain only noise, then the Noise Power Spectrum and the Noise Power Spectrum Variance are updated.

  7. Signal to Noise Ratio • Using the Noise Power Spectrum , the a priori SNR (ξk) and the a posteriori SNR (γk) are calculated. • a priori SNR: • γk=Rk2/λd(k) • where Rk is the modulus of the signal plus noise resultant spectral component • a posteriori SNR • ξk(n)=αG2γk(n-1)+(1- α)P [γk(n)-1] • where α = 0.99 and is a smoothing factor. • and G is the Gain Function from the MMSE • and P[x] is defined as x if x>0 or 0 otherwise

  8. Gain Calculation • The gain (G) of the signal is then updated using the Signal to Noise Ratios. • G= ξk/(1- ξk)e(η/2) • Where η= λdξk/(1- ξk)

  9. Signal Enhancement and Reconstruction • The signal is then cleaned by combining the FFT of each frame with the gain. • The signal is reconstructed using the overlap add method utilizing the phase of the FFT.

  10. Sample – Hair Dryer Background

  11. Sample – Jack Hammer Background

  12. Sample – Air Conditioner Background

  13. Sample – Cafeteria Background

  14. Sample – Automobile Background

  15. Sample – Coffee Grinder Background

  16. Sample – Fan Background

  17. Sample – Feedback Background

  18. Sample – White Noise Background

  19. Sample – Static Background

  20. References • Ephraim, Yariv. “Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator”. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 32, No. 6, December 1984

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