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- 1. DSP Lecture 1: Signals
- 2. DSP Lecture 2: Linear, time-invariant systems
- 3. DSP Lecture 3: Convolution and its properties
- 4. DSP Lecture 4: The Fourier Series
- 5. DSP Lecture 5: the Fourier Transform
- 6. DSP Lecture 6: Frequency Response
- 7. DSP Lecture 7: The Discrete-Time Fourier Transform
- 8. DSP Lecture 8: Introduction to the z-Transform
- 9. DSP Lecture 9: Inverse z-Transform; Poles and Zeros
- 10. DSP Lecture 10: The Discrete Fourier Transform
- 11. DSP Lecture 11: Exam 1 Review
- 12. DSP Lecture 12: Radix-2 Fast Fourier Transforms
- 13. DSP Lecture 13: The Cooley-Tukey and Good-Thomas FFTs
å¾å
- 14. DSP Lecture 14: The Sampling Theorem
- 15. DSP Lecture 15: Continuous-time filtering with digital systems; upsampling and downsampling
- 16. DSP Lecture 16: Multirate signal processing and polyphase representations
- 17. DSP Lecture 17: FIR filter design using least-squares
- 18. DSP Lecture 18: FIR filter design (Chebyshev)
- 19. DSP Lecture 19: IIR filter design
- 20. DSP Lecture 20: Introduction to adaptive filtering; ARMA processes
- 21. DSP Lecture 21: The Wiener filter
- 22. DSP Lecture 22a: Exam 2 format/review
- 23. DSP Lecture 22: Gradient descent and LMS
- 24. DSP Lecture 23: Least squares and recursive least squares
- 25. DSP Lecture 24: Introduction to quantization
- 26. DSP Lecture 25: Differential quantization and vocoding
- 27. DSP Lecture 26: Perfect reconstruction filter banks and intro to wavelets