Enhancement of Speech in Noisy Conditions Project Presentation Paul Coffey

1. Enhancement of Speech in Noisy Conditions Project Presentation Paul Coffey

2. Contents Project Overview System Development Testing Conclusion Project Presentation

3. Project Overview • This project is about The “Enhancement of Speech in Noisy Conditions” • Speech Enhancement is used in communications everywhere • From Mobile Phones to Speech Recognition Systems Project Presentation

4. Noisy Signal Analysis Window Fourier Transform Spectral Modification Inverse Fourier Transform Synthesis Window Overlap Add Enhanced Signal Project Overview • To Investigate Spectral Subtraction • A method to improve the quality of a signal that has been effected by noise • This is done by taking noise from degraded signal Project Presentation

5. Noisy Signal Speech and noise power spectrum estimation PXX(f) PNN(f) Wiener Filtering Enhanced Signal Project Overview • Implement Wiener Filter Method • Is derived and Works on same basis as Spectral Subtraction Project Presentation

6. System Development • To begin development, a simple task such as doubling the magnitude was implemented • Also getting the system to analyse and synthesise the whole signal • Hamming Windows and Overlap Add were also introduced to the system Project Presentation

7. System Development • Finally a basic Spectral Subtraction implementation of the filter was then achieved • Result from running the filter is shown • MagY = MagX – MagNN; Project Presentation

8. System Development • The other filter that was developed was the Wiener Filter • This was approached in the same way as the previous filter • Since this method is similar to the way it is derived as Spectral Subtraction, this greatly helped with speeding up development Project Presentation

9. System Development • Basic Wiener Filter Implementation • Result of the signal being filtered shown • This is implemented using the code below: W = MagX./(MagX+MagNN); MagY = MagX .* W; Project Presentation

10. System Development • Noise Detection and SNR were also needed to be developed • These were required for the filters to operate properly • Finding the energy levels of the signal was used to implement the noise detection method • The code to do so is: E = sum (x.^2); • The SNR is then was developed using the equation Project Presentation

11. Testing • Throughout the project tests were being carried out to see how the filters were working • Spectral Subtraction here with SNR of 10db Project Presentation

12. Testing • Here is the Wiener Filter Implementation with SNR at 10db • It is the same speech signal as used in the Spectral Subtraction implementation • Can see Wiener doesn’t cut out speech like the Spectral did Project Presentation

13. Testing • A blind test was carried out involving ten student in the Electronics Department • Consisted of number of different samples at different levels of SNR • Results from this test showed at low levels of SNR the two filters are very close • Then at higher levels of SNR the Wiener Filter is preferred Project Presentation

14. Conclusion • Speech Enhancement techniques are a very important part of Communication Systems • The two techniques, Spectral Subtraction and the Wiener Filter were implemented • From the testing the Wiener Filter was the preferred choice over the Spectral Subtraction Project Presentation

15. Questions? Project Presentation