The noise eliminator using fast fourier transform in ccstudios and matlab
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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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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

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


Preparation meetings

Preparation Meetings


Meetings contd

Meetings contd.


Implementing in matlab

Implementing in MatLab


Steps followed in matlab

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


Matlab graph of noisy signal

MatLab Graph of Noisy Signal


Matlab code

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);


Matlab graph of filtered signal

MatLab Graph of Filtered Signal


Matlab graph of noisy and filtered signal

MatLab Graph of Noisy and Filtered Signal


Dsk implementation

DSK Implementation


Ccstudio

CCStudio


Isrs c in cc studio

ISRs.c in CC Studio

#include "..\..\..\Common_Code\DSK_Config.h“

#include "math.h"

#include "frames.h"

// frame buffer declarations

#define BUFFER_COUNT1024 // 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;


What we learned

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.


Learning contd

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)


Conclusion

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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