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The Noise Eliminator Using Fast Fourier Transform in CCStudios and MatLab

The Noise Eliminator Using Fast Fourier Transform in CCStudios and MatLab. Presented to: Dr Li By: Megan Myles & David Jackson. The Noise Eliminator. Researching Project Idea Visited Various DSP Websites Implemented idea in MatLab to test the concept

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The Noise Eliminator Using Fast Fourier Transform in CCStudios and MatLab

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  1. The Noise Eliminator Using Fast Fourier Transform in CCStudios and MatLab Presented to: Dr Li By: Megan Myles & David Jackson

  2. The Noise Eliminator Researching Project Idea • Visited Various DSP Websites • Implemented idea in MatLab to test the concept • Interviewed Dr. Li and other engineers about the noise eliminator

  3. Preparation Meetings

  4. Meetings contd.

  5. Implementing in MatLab

  6. Steps followed in MatLab • Input wave file into MatLab as a vector • Run noisy signal through a Fourier Transform • Change all values of the signal points to zero except the first and last few. • Run signal through an inverse Fourier transform • Take only real part of ifft • This final signal will be the clear signal extracted from the noise

  7. MatLab Graph of Noisy Signal

  8. MatLab Code fs=22050; s=20000; x=wavread('5000noisywav.wav'); wavplay(x,fs); f=fft(x); f(s+1:220500-s,:)=0; i=real(ifft(f)); wavplay(i,fs);

  9. MatLab Graph of Filtered Signal

  10. MatLab Graph of Noisy and Filtered Signal

  11. DSK Implementation

  12. CCStudio

  13. ISRs.c in CC Studio #include "..\..\..\Common_Code\DSK_Config.h“ #include "math.h" #include "frames.h" // frame buffer declarations #define BUFFER_COUNT 1024 // buffer length in McBSP samples (L+R) #define BUFFER_LENGTH BUFFER_COUNT*2 // two shorts read from s = 100; for(i=s;i < BUFFER_COUNT-s;i++){ x[i].real = 0.0; x[i].imag = 0.0;

  14. What We Learned • Fast Fourier Transform - Chapter 8 FFT is a fast way for computers to calculate the Fourier transform. The difference between the FFT and IFFT is simply the division by N and the negative powers of the twiddle factors. The algorithm can be used for both the FFT and IFFT.

  15. Learning Contd. • Using the FFT for filtering As the order of a filter increases the time required to calculate the output value associated with each input sample also increases. • Frame based filtering helps increase the overall efficiency of the filtering and reduces the time required to pass various samples. (Chapter 7)

  16. Conclusion Noise filters have many uses: • Filter engine noise from car audio • Filter static from cell phone transmissions • Even filter an image for better clarity • Noise filters improve the quality of life around the world

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