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#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 31 hours to complete

Suggested: 8-10 hours/week...

#### English

Subtitles: English

### Skills you will gain

Signal ProcessingDiscrete Fourier TransformData TransmissionIpythonFourier AnalysisConvolutionLinear AlgebraDigital Signal Processing

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 31 hours to complete

Suggested: 8-10 hours/week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
7 hours to complete

## Module 1: Basics of Digital Signal Processing

7 videos (Total 93 min), 7 readings, 1 quiz
7 videos
1.1.a Introduction to digital signal processing25m
1.2.a Discrete-time signals16m
1.3.a How your PC plays discrete-time sounds3m
1.3.b The Karplus-Strong algorithm19m
Signal of the Day: Goethe's temperature measurement8m
1.4.a Complex exponentials14m
Introduction to Module 15m
Introduction to Lecture 1.15m
Introduction to Lecture 1.25m
Introduction to Lecture 1.35m
Introduction to Lecture 1.45m
Notes and external resources15m
Practice homework for Module 11h
1 practice exercise
Homework for Module 11h 45m
Week
2
5 hours to complete

## Module 2: Vector Spaces

6 videos (Total 63 min), 7 readings, 1 quiz
6 videos
Signal of the Day: Exoplanet hunting9m
2.2.a Vector space15m
2.2.b Signal spaces4m
2.3.a Bases13m
2.4.a Subspace-based approximations16m
Introduction to Module 25m
Introduction to Lecture 2.15m
Introduction to Lesson 2.25m
Introduction to Lecture 2.35m
Introduction to Lecture 2.45m
Notes and external resources15m
Practice homework for Module 21h
1 practice exercise
Homework for Module 21h 30m
Week
3
9 hours to complete

## Module 3: Part 1 - Basics of Fourier Analysis

14 videos (Total 95 min), 11 readings, 1 quiz
14 videos
3.1.b The DFT as a change of basis10m
3.2.a DFT definition5m
3.2.b Examples of DFT calculation13m
3.2.c Interpreting a DFT plot4m
3.3.a DFT analysis11m
3.3.b DFT example - analysis of musical instruments4m
3.3.c DFT synthesis5m
3.3.d DFT example - tide prediction in Venice4m
3.3.e DFT example - MP3 compression4m
Signal of the Day: The first man-made signal from outer space5m
3.4.a The short-time Fourier transform5m
3.4.b The spectrogram7m
3.4.c Time-frequency tiling5m
Introduction to Module 310m
Introduction to Lesson 3.15m
Summary of Lesson 3.110m
Introduction to Lesson 3.25m
Summary of Lesson 3.210m
Introduction to Lesson 3.35m
Summary of Lesson 3.310m
Real-valued Transforms10m
Introduction to Lesson 3.45m
Summary of Lesson 3.410m
Practice homework for Module 3 Part 11h
1 practice exercise
Homework for Module 3 Part 12h
Week
4
8 hours to complete

## Module 3: Part 2 - Advanced Fourier Analysis

10 videos (Total 116 min), 7 readings, 1 quiz
10 videos
3.5.b Karplus-Strong revisited and DFS7m
3.6.a Karplus-Strong revisited and the DTFT13m
3.6.b Existence and properties of the DTFT7m
3.6.c The DTFT as a change of basis9m
3.7.a Sinusoidal modulation7m
3.7.b Tuning a guitar4m
Signal of the Day: Tristan Chord6m
3.8* Relationship between transforms14m
3.9* The fast Fourier transform42m
Introduction to Lesson 3.55m
Summary of Lesson 3.510m
Introduction to Lesson 3.65m
Summary of Lesson 3.610m
Introduction to Lesson 3.75m
Notes and external ressources15m
Practice homework for Module 3 Part 21h
1 practice exercise
Homework for Module 3 Part 22h
4.7
108 Reviews

## 50%

started a new career after completing these courses

## 44%

got a tangible career benefit from this course

## 17%

got a pay increase or promotion

### Top reviews from Digital Signal Processing

By JAJul 2nd 2017

Definitely an interesting course that is explained well. The math gets to a pretty high level after a few sections, but it still seems manageable to understand. I would recommend it for sure!

By GCOct 2nd 2018

A good course with awesome instructors, good for learning the basic level of signal processing. It will initiate you for more advanced levels of signal processing. Fun experience overall.

## Instructors

### Paolo Prandoni

Lecturer
School of Computer and Communication Science

### Martin Vetterli

Professor
School of Computer and Communication Sciences