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Digital Signal Processing 2: Filtering
  • Level Professional
  • Duration 18 hours
  • Course by École Polytechnique Fédérale de Lausanne
  • Offered by

About

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

Modules

Welcome to DSP Two!

    • 1 Readings
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1 Readings

  • Welcome to DSP Two!

Lesson 2.1.1: Linear Filters

    • 2 Videos
    • 2 Readings
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2 Videos

  • 2.1.1.a Linear time-invariant filters
  • 2.1.1.b Convolution

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.1.2: Filtering by Example

    • 2 Videos
    • 2 Readings
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2 Videos

  • 2.1.2.a The moving average filter
  • 2.1.2.b The leaky integrator

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.1.3: Filter Stability

    • 2 Videos
    • 2 Readings
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2 Videos

  • 2.1.3.a Filter classification in the time domain
  • 2.1.3.b Filter stability

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.1.4: Frequency Response

    • 2 Videos
    • 2 Readings
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2 Videos

  • 2.1.4.a The convolution theorem
  • 2.1.4.b Examples of frequency response

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.1.5: Ideal Filters

    • 4 Videos
    • 2 Readings
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4 Videos

  • 2.1.5.a Filter classification in the frequency domain
  • 2.1.5.b The ideal lowpass filter
  • 2.1.5.c Ideal filters derived from the ideal lowpass filter
  • 2.1.5.d Demodulation revisited

2 Readings

  • Introduction
  • What have we learned?

Module 2.1 - Assignments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Homework for Module 2.1

1 Readings

  • Practice homework

Notes and supplementary material

    • 1 Videos
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1 Videos

  • SOTD: Can one hear the shape of a room?

Python Notebooks

    • 1 Plugin
    • 2 Lab
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2 Labs

  • Filtering Music
  • A Taste of Nonlinear Processing

Lesson 2.2.1: Basic Filter Design

    • 3 Videos
    • 2 Readings
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3 Videos

  • 2.2.1.a Impulse truncation (and the Gibbs phenomenon)
  • 2.2.1.b The window method
  • 2.2.1.c Frequency sampling

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.2.2: The z-transform

    • 2 Videos
    • 2 Readings
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2 Videos

  • 2.2.2.a The z-transform
  • 2.2.2.b Region of convergence and stability

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.2.3: "Intuitive" Filter Design

    • 1 Videos
    • 2 Readings
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1 Videos

  • 2.2.3 Intuitive IIR designs

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.2.4: Classic Filter Design Methods

    • 4 Videos
    • 2 Readings
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4 Videos

  • 2.2.4.a Filter specifications
  • 2.2.4.b IIR design
  • 2.2.4.c FIR design
  • 2.2.4.d Fractional delay and Hilbert filter

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.2.5: Implementation of digital filters

    • 2 Videos
    • 1 Readings
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2 Videos

  • 2.2.5.a Implementation of digital filters
  • 2.2.5.b Real-time processing

1 Readings

  • Introduction

Assignments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Homework for Module 2.2

1 Readings

  • Practice homework

Notes and Supplementary Materials

    • 1 Videos
    • 1 Readings
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1 Videos

  • Signal of the Day: Image Resolution and Space Exploration

1 Readings

  • Notes and Supplementary Materials

Python Notebooks

    • 1 Plugin
    • 2 Lab
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2 Labs

  • FIR Implementation
  • Parks-McClellan FIR Design Algorithm

Lesson 2.3.1: Stochastic Signal Processing

    • 4 Videos
    • 2 Readings
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4 Videos

  • 2.3.1.a Random Variables
  • 2.3.1.b Stochastic Processes
  • 2.3.1.c Power Spectral Density
  • 2.3.1.d Filtering Random Processes

2 Readings

  • Introduction
  • What have we learned?

Lesson 2.3.2: Adaptive Signal Processing

    • 4 Videos
    • 2 Readings
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4 Videos

  • 2.3.2.a Optimal Least Squares
  • 2.3.2.b LPC Speech Coding
  • 2.3.2.c The LMS Filter
  • 2.3.2.d Echo Cancellation

2 Readings

  • Introduction
  • What have we learned?

Module 2.3: Assignments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Homework for Module 2.3

1 Readings

  • Practice Homework

Module 2.3: Notes and Supplementary Materials

    • 1 Readings
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1 Readings

  • Notes and Supplementary Material

Python Notebooks

    • 1 Plugin
    • 1 Lab
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1 Labs

  • Echo Cancellation

Auto Summary

Embark on a comprehensive journey into the world of Digital Signal Processing (DSP) with "Digital Signal Processing 2: Filtering." This advanced course, offered by Coursera, delves into the core principles that have revolutionized communication and entertainment technologies. Ideal for professionals in the Science & Engineering domain, this course covers essential DSP concepts, including discrete-time signals, Fourier analysis, filter design, sampling, interpolation, and quantization. Guided by expert instruction, learners will bridge the gap between theory and real-world applications through hands-on examples and demonstrations. The course is structured to provide a robust DSP toolkit, enabling participants to analyze practical communication systems meticulously. Spanning 1080 minutes, the course is designed for those with a solid foundation in basic calculus and linear algebra. Programming examples, primarily in Python, are provided to enhance understanding, though learners are welcome to use their preferred programming languages. Available through both Starter and Professional subscription plans, this course caters to dedicated professionals eager to deepen their expertise in digital signal processing and advance their careers in this dynamic field.

Paolo Prandoni
Instructors

Paolo Prandoni

Martin Vetterli
Instructors

Martin Vetterli