This course is part of the Digital Signal Processing Specialization

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Paolo Prandoni +1 more instructor

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Digital Signal Processing SpecializationÉcole Polytechnique Fédérale de Lausanne

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 1 of 4 in the

Digital Signal Processing Specialization

Intermediate Level

Approx. 28 hours to complete

English

What you will 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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

Digital Signal Processing Specialization

Intermediate Level

Approx. 28 hours to complete

English

Instructors

Instructor rating

4.7/5 (183 Ratings)

Paolo Prandoni

Lecturer

School of Computer and Communication Science

107,070 Learners

5 Courses

Martin Vetterli

Professor

School of Computer and Communication Sciences

107,070 Learners

5 Courses

Offered by

École Polytechnique Fédérale de Lausanne

Syllabus - What you will learn from this course

Content Rating95%(3,383 ratings)

Week1

Week 1

7 hours to complete

Module 1.1: Digital Signal Processing: the Basics

7 hours to complete

6 videos (Total 88 min), 7 readings, 1 quiz

6 videos

1.1.1 What is digital signal processing?25m1.1.2 Discrete-time signals16m1.1.3.a How your PC plays discrete-time sounds3m1.1.3.b The Karplus-Strong algorithm19m1.1.4 Complex exponentials14mSignal of the Day: Goethe's temperature measurement8m

7 readings

Welcome to DSP One!5mIntroduction5mIntroduction2mIntroduction5mIntroduction5mPractice homework1hNotes and Supplementary Material15m

1 practice exercise

Homework for Module 1.11h 45m

Week2

Week 2

5 hours to complete

Module 1.2: Signal Processing Meets Vector Space

5 hours to complete

7 videos (Total 63 min), 6 readings, 1 quiz

Week3

Week 3

9 hours to complete

Module 1.3: Fourier Analysis: the Basics

9 hours to complete

14 videos (Total 94 min), 10 readings, 1 quiz

14 videos

1.3.1.a The frequency domain5m1.3.1.b The DFT as a change of basis10m1.3.2.a DFT definition5m1.3.2.b Examples of DFT calculation13m1.3.2.c Interpreting a DFT plot4m1.3.3.a DFT analysis11m1.3.3.b DFT example - analysis of musical instruments4m1.3.3.c DFT synthesis5m1.3.3.d DFT example - tide prediction in Venice4m1.3.3.e DFT example - MP3 compression4m1.3.4.a The short-time Fourier transform5m1.3.4.b The spectrogram7m1.3.4.c Time-frequency tiling5mSignal of the Day: The first man-made signal from outer space5m

10 readings

Introduction6mWhat have we learned?5mIntroduction1mWhat have we learned?5mIntroduction1mWhat have we learned?5mIntroduction1mWhat have we learned?5mPractice homework1hReal-valued Transforms20m

1 practice exercise

Homework for Module 1.32h

Week4

Week 4

8 hours to complete

Module 1.4: Fourier Analysis: More Advanced Tools

8 hours to complete

11 videos (Total 121 min), 9 readings, 1 quiz

11 videos

1.4.1.a Discrete Fourier series4m1.4.1.b Karplus-Strong revisited and DFS7m1.4.2.a Karplus-Strong revisited and the DTFT13m1.4.2.b Existence and properties of the DTFT7m1.4.2.c The DTFT as a change of basis10m1.4.3.a Sinusoidal modulation7m1.4.3.b Tuning a guitar4m1.4.4.a Relationships between transforms11m1.4.4.b Zero padding5m1.4.5 The fast Fourier transform42mSignal of the Day: Tristan Chord6m

9 readings

Introduction1mWhat have we learned?2mIntroduction1mWhat have we learned?10mIntroduction5mIntroduction1mIntroduction2mPractice homework1hNotes and Supplementary Material15m

1 practice exercise

Homework for Module 1.42h

Reviews

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TOP REVIEWS FROM DIGITAL SIGNAL PROCESSING 1: BASIC CONCEPTS AND ALGORITHMS

by MFNov 3, 2020

If you have enough patience to go through the math, you'll find this course and the subsequent courses in the specialization extremely rewarding.

by NCNov 1, 2021

I material the very challenging, but with some time and effort it can be very fun and also rewarding

by EEJul 18, 2022

One of the mathematically engaging courses I have ever taken on Coursera. Be reminded to have patience to go through the math. Good luck.

by FWAug 20, 2020

Lecture explains concepts very clearly. Python labs provide very good hands-on experience.

View all reviews

About the Digital Signal Processing Specialization

This Specialization provides a full course in Digital Signal Processing, with a focus on audio processing and data transmission. You will start from the basic concepts of discrete-time signals and proceed to learn how to analyze data via the Fourier transform, how to manipulate data via digital filters and how to convert analog signals into digital format. Finally, you will also discover how to implement real-time DSP algorithms on a general-purpose microcontroller. The solid theoretical bases provided by the four courses in this specialization are complemented by applied examples in Python, in the form of Jupyter Notebooks; exercises with solutions provide a wealth of examples in order to tackle the weekly homework.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Digital Signal Processing 1: Basic Concepts and Algorithms

About this Course

What you will learn