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.

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Shareable Certificate

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Course 1 of 4 in the

Digital Signal Processing Specialization

Intermediate Level

Approx. 29 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

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.

Course 1 of 4 in the

Digital Signal Processing Specialization

Intermediate Level

Approx. 29 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.71/5 (175 Ratings)

Paolo Prandoni

Lecturer

School of Computer and Communication Science

103,215 Learners

5 Courses

Martin Vetterli

Professor

School of Computer and Communication Sciences

103,215 Learners

5 Courses

Offered by

École Polytechnique Fédérale de Lausanne

Syllabus - What you will learn from this course

Content Rating96%(3,014 ratings)

Week

Week 1

7 hours to complete

Module 1.1: Digital Signal Processing: the Basics

Introduction to the notation and basics of Digital Signal Processing

7 hours to complete

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

6 videos

1.1.1 What is digital signal processing?25m

1.1.2 Discrete-time signals16m

1.1.3.a How your PC plays discrete-time sounds3m

1.1.3.b The Karplus-Strong algorithm19m

1.1.4 Complex exponentials14m

Signal of the Day: Goethe's temperature measurement8m

7 readings

Welcome to DSP One!5m

Introduction5m

Introduction2m

Introduction5m

Practice homework1h

Notes and Supplementary Material15m

1 practice exercise

Homework for Module 1.11h 45m

Week

Week 2

5 hours to complete

Module 1.2: Signal Processing Meets Vector Space

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

5 hours to complete

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

Week

Week 3

9 hours to complete

Module 1.3: Fourier Analysis: the Basics

The fundamental concepts behind the Fourier transform and the frequency domain

9 hours to complete

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

14 videos

1.3.1.a The frequency domain5m

1.3.1.b The DFT as a change of basis10m

1.3.2.a DFT definition5m

1.3.2.b Examples of DFT calculation13m

1.3.2.c Interpreting a DFT plot4m

1.3.3.a DFT analysis11m

1.3.3.b DFT example - analysis of musical instruments4m

1.3.3.c DFT synthesis5m

1.3.3.d DFT example - tide prediction in Venice4m

1.3.3.e DFT example - MP3 compression4m

1.3.4.a The short-time Fourier transform5m

1.3.4.b The spectrogram7m

1.3.4.c Time-frequency tiling5m

Signal of the Day: The first man-made signal from outer space5m

10 readings

Introduction6m

What have we learned?5m

Introduction1m

What have we learned?5m

Introduction1m

What have we learned?5m

Introduction1m

What have we learned?5m

Practice homework1h

Real-valued Transforms20m

1 practice exercise

Homework for Module 1.32h

Week

Week 4

8 hours to complete

Module 1.4: Fourier Analysis: More Advanced Tools

Delving deeper in the world of Fourier analysis.

8 hours to complete

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

11 videos

1.4.1.a Discrete Fourier series4m

1.4.1.b Karplus-Strong revisited and DFS7m

1.4.2.a Karplus-Strong revisited and the DTFT13m

1.4.2.b Existence and properties of the DTFT7m

1.4.2.c The DTFT as a change of basis10m

1.4.3.a Sinusoidal modulation7m

1.4.3.b Tuning a guitar4m

1.4.4.a Relationships between transforms11m

1.4.4.b Zero padding5m

1.4.5 The fast Fourier transform42m

Signal of the Day: Tristan Chord6m

9 readings

Introduction1m

What have we learned?2m

Introduction1m

What have we learned?10m

Introduction5m

Introduction1m

Introduction2m

Practice homework1h

Notes and Supplementary Material15m

1 practice exercise

Homework for Module 1.42h

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

by FWAug 20, 2020

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

by SMOct 30, 2020

The math was extremely difficult. But I made it through.

by SEOct 15, 2021

I think it's quite good for one who is interested in signal processing fields and it helps you broaden your vision.

by TSAug 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.

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