What will I actually learn in this digital signal processing course?

You'll learn how to think about discrete-time signals, represent them mathematically, and analyze them in the frequency domain. It starts with the basics of signals and simple DSP operations, then builds into vector-space thinking and Fourier analysis. Along the way, you'll apply the ideas through guided examples such as sound synthesis and reading DFT plots.

Do I need calculus or linear algebra before starting this course?

Yes, basic calculus and linear algebra are recommended before you start. The course uses linear algebra directly when it treats signals as vectors and bases, and that math carries into the Fourier material. You don't need deep programming experience, but several examples are provided as Python notebooks for testing the algorithms.

Is this course beginner-friendly for digital signal processing?

It's a good fit for beginners in DSP if you already have some math background. The course starts from the definition of a discrete-time signal, but it moves fairly quickly into vector spaces and Fourier methods rather than staying at an intuitive overview level. If you're completely new to calculus, linear algebra, or math-heavy engineering courses, it may feel demanding.

How long does it take to complete this course?

Plan on about 29 hours total. At around 10 hours a week, that's roughly three weeks of steady study, with time split across lessons, readings, and worked examples. The course also includes practice homework, quizzes, and guided notebook labs.

Are there hands-on exercises or labs in this course?

Yes, but the hands-on work is guided rather than project-based. You'll work through Python notebook labs and practice exercises that let you test ideas such as the Karplus-Strong algorithm, plot Fourier results, or examine signal examples like dial tones. That practice helps you connect the math to signals you can hear, visualize, and interpret.

What skills, topics, or methods are covered in this course?

You'll cover discrete-time signals, the vector-space view of signals, and frequency-domain analysis. The course teaches core Fourier tools such as the DFT and STFT, then uses them to interpret audio, speech, and communication signals. Overall, it helps you move between time and frequency representations and reason about how digital signals behave.

What can I actually do after finishing this course?

After finishing, you should be able to analyze sampled signals with common Fourier representations and explain them using the language of vectors, bases, and frequency. For example, you could inspect a DFT plot to identify dominant components in a musical sound or another discrete-time signal. That's a solid outcome if your goal is to interpret and analyze signals in a principled way.

Is this course more focused on theory or hands-on learning?

It's more concept-focused, with guided practice to reinforce the ideas. Most of the course is spent understanding how signals, frequency analysis, and the underlying math fit together, while the labs and exercises let you test those ideas on concrete examples.

Why would I choose this course over other digital signal processing courses?

This course is a strong choice if you want DSP explained through linear algebra and Fourier thinking, not just as a toolbox of formulas. It moves from discrete-time signals into vector spaces and frequency-domain analysis, then backs that up with guided notebooks and examples from audio and communications. If you want a mathematically serious introduction with enough practice to see the ideas in action, this course is likely a better fit than a lighter, more tool-driven overview.

Digital Signal Processing 1: Basic Concepts and Algorithms

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Digital Signal Processing 1: Basic Concepts and Algorithms

This course is part of Digital Signal Processing Specialization

Instructors: Paolo Prandoni

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4 modules

Gain insight into a topic and learn the fundamentals.

646 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

646 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

What you'll learn

The nature of discrete-time signals
Discrete-time signals are vectors in a vector space
Discrete-time signals can be analyzed in the frequency domain via the Fourier transform

Skills you'll gain

Tools you'll learn

Jupyter

Details to know

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Assessments

4 assignments

Taught in English

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There are 4 modules in this course

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.

In this series of four courses, you will 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.

Module details

Introduction to the notation and basics of Digital Signal Processing

What's included

6 videos7 readings1 assignment2 ungraded labs1 plugin

6 videosTotal 88 minutes

1.1.1 What is digital signal processing?25 minutes
1.1.2 Discrete-time signals16 minutes
1.1.3.a How your PC plays discrete-time sounds4 minutes
1.1.3.b The Karplus-Strong algorithm20 minutes
1.1.4 Complex exponentials15 minutes
Signal of the Day: Goethe's temperature measurement9 minutes

7 readingsTotal 97 minutes

Welcome to DSP One!5 minutes
Introduction5 minutes
Introduction2 minutes
Introduction5 minutes
Introduction5 minutes
Practice homework60 minutes
Notes and Supplementary Material15 minutes

1 assignmentTotal 105 minutes

Homework for Module 1.1105 minutes

2 ungraded labsTotal 120 minutes

Transoceanic Signal Transmission60 minutes
The Karplus-Strong Algorithm60 minutes

1 pluginTotal 15 minutes

Feedback W115 minutes

Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.

What's included

7 videos6 readings1 assignment1 ungraded lab1 plugin

7 videosTotal 63 minutes

1.2.1 Signal processing and vector spaces4 minutes
1.2.2.a Vector space15 minutes
1.2.2.b Signal spaces5 minutes
1.2.3 Bases14 minutes
1.2.4.a Subspace-based approximations5 minutes
1.2.4.b Polynomial Approximation12 minutes
Signal of the Day: Exoplanet hunting9 minutes

6 readingsTotal 79 minutes

Introduction1 minute
Introduction1 minute
Introduction1 minute
Introduction1 minute
Practice homework60 minutes
Notes and Supplementary Material15 minutes

1 assignmentTotal 90 minutes

Homework for Module 1.290 minutes

1 ungraded labTotal 60 minutes

Haar Bases for Image Compression60 minutes

1 pluginTotal 15 minutes

Feedback W215 minutes

The fundamental concepts behind the Fourier transform and the frequency domain

What's included

14 videos10 readings1 assignment3 ungraded labs1 plugin

14 videosTotal 94 minutes

1.3.1.a The frequency domain5 minutes
1.3.1.b The DFT as a change of basis11 minutes
1.3.2.a DFT definition5 minutes
1.3.2.b Examples of DFT calculation14 minutes
1.3.2.c Interpreting a DFT plot5 minutes
1.3.3.a DFT analysis11 minutes
1.3.3.b DFT example - analysis of musical instruments5 minutes
1.3.3.c DFT synthesis6 minutes
1.3.3.d DFT example - tide prediction in Venice4 minutes
1.3.3.e DFT example - MP3 compression5 minutes
1.3.4.a The short-time Fourier transform5 minutes
1.3.4.b The spectrogram8 minutes
1.3.4.c Time-frequency tiling6 minutes
Signal of the Day: The first man-made signal from outer space6 minutes

10 readingsTotal 109 minutes

Introduction6 minutes
What have we learned?5 minutes
Introduction1 minute
What have we learned?5 minutes
Introduction1 minute
What have we learned?5 minutes
Introduction1 minute
What have we learned?5 minutes
Practice homework60 minutes
Real-valued Transforms20 minutes

1 assignmentTotal 120 minutes

Homework for Module 1.3120 minutes

3 ungraded labsTotal 180 minutes

How to Plot the DFT60 minutes
DFT and Numerical Precision60 minutes
Dial Tones60 minutes

1 pluginTotal 15 minutes

Feedback W315 minutes

Delving deeper in the world of Fourier analysis.

What's included

11 videos9 readings1 assignment2 ungraded labs1 plugin

11 videosTotal 121 minutes

1.4.1.a Discrete Fourier series5 minutes
1.4.1.b Karplus-Strong revisited and DFS8 minutes
1.4.2.a Karplus-Strong revisited and the DTFT13 minutes
1.4.2.b Existence and properties of the DTFT7 minutes
1.4.2.c The DTFT as a change of basis10 minutes
1.4.3.a Sinusoidal modulation7 minutes
1.4.3.b Tuning a guitar5 minutes
1.4.4.a Relationships between transforms12 minutes
1.4.4.b Zero padding5 minutes
1.4.5 The fast Fourier transform42 minutes
Signal of the Day: Tristan Chord7 minutes

9 readingsTotal 97 minutes

Introduction1 minute
What have we learned?2 minutes
Introduction1 minute
What have we learned?10 minutes
Introduction5 minutes
Introduction1 minute
Introduction2 minutes
Practice homework60 minutes
Notes and Supplementary Material15 minutes

1 assignmentTotal 120 minutes

Homework for Module 1.4120 minutes

2 ungraded labsTotal 120 minutes

Can You Hear the Phase of a Sound?60 minutes
Audio Frequency Beatings60 minutes

1 pluginTotal 15 minutes

Feedback W415 minutes

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Instructors

Instructor ratings

(222 ratings)

Paolo Prandoni

École Polytechnique Fédérale de Lausanne

5 Courses132,010 learners

Offered by

École Polytechnique Fédérale de Lausanne

Learner reviews

5 stars
69.34%
4 stars
20.58%
3 stars
5.57%
2 stars
1.70%
1 star
2.78%

Showing 3 of 646

Reviewed on Aug 16, 2020

It offers rigorous introduction to DSP. Besides the lectures, it requires separate study of the materials to get well acquainted with the concepts.

Reviewed on Jul 21, 2020

very good course, but it require some math and a brief reading of a book in signals, there are only few courses in coursera that are challenging, this is one of them, 10/10

Reviewed on Nov 28, 2022

This course was a great refresher after not working with DSP concepts for while. It also went beyond what I had learned in school with good explanations and extra insights.

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