Understanding the Discrete Fourier Transform - Week 14

Understanding the Discrete Fourier Transform - Week 14

The Julia Programming Language via YouTube Direct link

Drawing waveforms

4 of 32

4 of 32

Drawing waveforms

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Classroom Contents

Understanding the Discrete Fourier Transform - Week 14

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  1. 1 Introduction
  2. 2 Time series data from sound recordings
  3. 3 Julia notebook: Playing with sound - WAV files
  4. 4 Drawing waveforms
  5. 5 Effect of frequency
  6. 6 Combining (superposing) different frequencies
  7. 7 Julia: FFT function
  8. 8 Discrete Fourier Transform (DFT) vs Fast Fourier Transform (FFT)
  9. 9 Plotting an FFT
  10. 10 Musical overtones: Magnitude of the FFT
  11. 11 Analyzing a sound file using the FFT
  12. 12 Defining the DFT mathematically
  13. 13 First term of the DFT
  14. 14 Visualizing the DFT in the complex plane
  15. 15 Equally-spaced points on unit circle in the complex plane
  16. 16 Idea of Fourier transform of a signal: walking around a circle
  17. 17 Adding complex numbers as adding vectors
  18. 18 Magnitude of DFT gives information about frequency
  19. 19 Angle of DFT gives information about phase
  20. 20 Interpreting the second term of the DFT
  21. 21 General formula for DFT
  22. 22 Implementing the DFT in Julia
  23. 23 Julia: Writing "i" as im
  24. 24 Julia: Array comprehension
  25. 25 Comparison of DFT with FFT results
  26. 26 Julia: isapprox for testing approximate equality
  27. 27 Efficiency of the implementation
  28. 28 Pre-computing an array of powers
  29. 29 Julia: Modulo (%)
  30. 30 Julia: OffsetArray for zero-based indexing
  31. 31 Computational complexity of DFT vs FFT
  32. 32 DFT as polynomials

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