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DSP Course Lessons Index

This document provides a comprehensive index of all 28 lessons in the DSP course with links to the corresponding YouTube lectures and lesson directories.

How to Use This Index

Each lesson includes:

  • YouTube Link: Direct link to Professor Radke's video lecture
  • Lesson Directory: Path to the lesson folder in this repository
  • Topics Covered: Key concepts and techniques
  • Python Libraries: Main Python tools used in the examples

Lessons Overview

Part I: Fundamentals

Lesson 1: Discrete-time Signals

  • Video: DSP Lecture 1
  • Topics: Signal types, continuous vs discrete, unit step, unit impulse, exponentials
  • Libraries: NumPy, Matplotlib

Lesson 1a: Python for DSP

  • Video: DSP Lecture 1a
  • Topics: Python basics, NumPy arrays, SciPy signal processing, Matplotlib plotting
  • Libraries: NumPy, SciPy, Matplotlib, Jupyter

Lesson 2: Linear, Time-Invariant Systems

  • Video: DSP Lecture 2
  • Topics: LTI systems, impulse response, system properties (linearity, time-invariance)
  • Libraries: NumPy, SciPy.signal

Lesson 3: Convolution and its Properties

  • Video: DSP Lecture 3
  • Topics: Discrete convolution, convolution properties, implementation techniques
  • Libraries: NumPy, SciPy.signal

Lesson 4: The Fourier Series

  • Video: DSP Lecture 4
  • Topics: Periodic signals, Fourier series representation, synthesis and analysis
  • Libraries: NumPy, Matplotlib

Lesson 5: The Fourier Transform

  • Video: DSP Lecture 5
  • Topics: Continuous Fourier transform, transform pairs, properties
  • Libraries: NumPy, SciPy.fft

Lesson 6: Frequency Response

  • Video: DSP Lecture 6
  • Topics: Frequency response of LTI systems, magnitude and phase response
  • Libraries: SciPy.signal, Matplotlib

Lesson 7: The Discrete-Time Fourier Transform

  • Video: DSP Lecture 7
  • Topics: DTFT definition, properties, relationship to z-transform
  • Libraries: NumPy, SciPy.fft

Lesson 8: The z-Transform

  • Video: DSP Lecture 8
  • Topics: z-transform definition, region of convergence, common transforms
  • Libraries: NumPy, SciPy.signal

Lesson 9: The Inverse z-Transform; Poles and Zeros

  • Video: DSP Lecture 9
  • Topics: Inverse z-transform, pole-zero analysis, stability
  • Libraries: NumPy, SciPy.signal, Matplotlib

Lesson 10: The Discrete Fourier Transform

  • Video: DSP Lecture 10
  • Topics: DFT definition, circular convolution, DFT properties
  • Libraries: NumPy.fft, SciPy.fft

Part II: Fast Algorithms and Sampling

Lesson 10a: Exam 1 Review

  • Video: DSP Lecture 10a
  • Topics: Review of fundamental concepts, midterm preparation
  • Libraries: Review of NumPy, SciPy

Lesson 11: Radix-2 Fast Fourier Transforms

  • Video: DSP Lecture 11
  • Topics: FFT algorithm, computational complexity, decimation-in-time
  • Libraries: NumPy.fft

Lesson 12: The Cooley-Tukey and Good-Thomas FFTs

  • Video: DSP Lecture 12
  • Topics: Advanced FFT algorithms, mixed-radix FFT
  • Libraries: NumPy.fft, SciPy.fft

Lesson 13: The Sampling Theorem

  • Video: DSP Lecture 13
  • Topics: Nyquist rate, aliasing, reconstruction
  • Libraries: NumPy, SciPy.signal, Matplotlib

Lesson 14: Continuous-Time Filtering with Digital Systems; Upsampling and Downsampling

  • Video: DSP Lecture 14
  • Topics: Upsampling, downsampling, interpolation, decimation
  • Libraries: SciPy.signal.resample

Lesson 15: Multirate Signal Processing and Polyphase Representations

  • Video: DSP Lecture 15
  • Topics: Polyphase decomposition, efficient multirate systems
  • Libraries: SciPy.signal

Part III: Filter Design

Lesson 16: FIR Filter Design (Least-Squares)

  • Video: DSP Lecture 16
  • Topics: FIR filter design, windowing methods, least-squares approximation
  • Libraries: SciPy.signal.firwin

Lesson 17: FIR Filter Design (Chebyshev)

  • Video: DSP Lecture 17
  • Topics: Chebyshev approximation, Parks-McClellan algorithm
  • Libraries: SciPy.signal.remez

Lesson 18: IIR Filter Design

  • Video: DSP Lecture 18
  • Topics: Butterworth, Chebyshev, elliptic filters, bilinear transform
  • Libraries: SciPy.signal (butter, cheby1, cheby2, ellip)

Part IV: Adaptive Filters

Lesson 19: Introduction to Adaptive Filtering; ARMA Processes

  • Video: DSP Lecture 19
  • Topics: Introduction to adaptive filtering, ARMA models, statistical signal processing
  • Libraries: NumPy, SciPy.signal

Lesson 20: The Wiener Filter

  • Video: DSP Lecture 20
  • Topics: Optimal filtering, Wiener-Hopf equations, noise reduction
  • Libraries: NumPy, SciPy.signal

Lesson 21: Gradient Descent and LMS

  • Video: DSP Lecture 21
  • Topics: Adaptive filtering, LMS algorithm, convergence
  • Libraries: NumPy

Lesson 22: Least Squares and Recursive Least Squares

  • Video: DSP Lecture 22
  • Topics: RLS algorithm, exponential weighting, tracking
  • Libraries: NumPy

Lesson 22a: Exam 2 Review

  • Video: DSP Lecture 22a
  • Topics: Review of advanced concepts, final exam preparation
  • Libraries: Review of all previous topics

Part V: Quantization and Advanced Topics

Lesson 23: Introduction to Quantization

  • Video: DSP Lecture 23
  • Topics: Quantization noise, SNR, uniform quantization
  • Libraries: NumPy

Lesson 24: Differential Quantization and Vocoding

  • Video: DSP Lecture 24
  • Topics: DPCM, delta modulation, vocoders
  • Libraries: NumPy, SciPy

Lesson 25: Perfect Reconstruction Filter Banks

  • Video: DSP Lecture 25
  • Topics: Filter banks, subband coding, introduction to wavelets
  • Libraries: PyWavelets

Lesson 26: Wavelets (Part 1)

  • Video: DSP Lecture 26
  • Topics: Wavelet theory, continuous wavelet transform
  • Libraries: PyWavelets

Lesson 27: Wavelets (Part 2)

  • Video: DSP Lecture 27
  • Topics: Discrete wavelet transform, applications
  • Libraries: PyWavelets

Lesson 28: Advanced DSP Topics

  • Video: DSP Lecture 28
  • Topics: Modern signal processing, course review
  • Libraries: Various

Navigation Tips

  • Each lesson directory contains:

    • README.md: Detailed lesson information and learning objectives
    • examples/: Runnable Python scripts demonstrating concepts
    • exercises/: Practice problems with solutions
    • data/: Sample data files for demonstrations

  • Start with Lesson 1 and progress sequentially for best results

  • Most examples can be run independently

  • Refer to the main requirements.txt for all dependencies

Additional Resources