Digital Signal Processing 1: Basic Concepts and Algorithms

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299 个评分

Paolo Prandoni 另外 +1 位授课教师

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课程信息

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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第 1 门课程（共 4 门）

数字信号处理专项课程

可灵活调整截止日期

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中级

完成时间大约为29 小时

英语（English）

字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）

您将学到的内容有

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

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 1 门课程（共 4 门）

数字信号处理专项课程

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为29 小时

英语（English）

讲师

授课教师评分

4.7/5 （98 个评分）

Paolo Prandoni

Lecturer

School of Computer and Communication Science

88,961 个学生

5 门课程

Martin Vetterli

Professor

School of Computer and Communication Sciences

88,961 个学生

5 门课程

提供方

洛桑联邦理工学院

教学大纲 - 您将从这门课程中学到什么

内容评分96%（1,880 个评分）

周

周 1

完成时间为 7 小时

Module 1.1: Digital Signal Processing: the Basics

Introduction to the notation and basics of Digital Signal Processing

完成时间为 7 小时

6 个视频（总计 88 分钟）, 7 个阅读材料, 1 个测验

6 个视频

1.1.1 What is digital signal processing?25分钟

1.1.2 Discrete-time signals16分钟

1.1.3.a How your PC plays discrete-time sounds3分钟

1.1.3.b The Karplus-Strong algorithm19分钟

1.1.4 Complex exponentials14分钟

Signal of the Day: Goethe's temperature measurement8分钟

7 个阅读材料

Welcome to DSP One!5分钟

Introduction5分钟

Introduction2分钟

Introduction5分钟

Practice homework1小时

Notes and Supplementary Material15分钟

1 个练习

Homework for Module 1.11 小时 45 分

周

周 2

完成时间为 5 小时

Module 1.2: Signal Processing Meets Vector Space

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

完成时间为 5 小时

7 个视频（总计 63 分钟）, 6 个阅读材料, 1 个测验

7 个视频

1.2.1 Signal processing and vector spaces4分钟

1.2.2.a Vector space15分钟

1.2.2.b Signal spaces4分钟

1.2.3 Bases13分钟

1.2.4.a Subspace-based approximations4分钟

1.2.4.b Polynomial Approximation11分钟

Signal of the Day: Exoplanet hunting9分钟

6 个阅读材料

Introduction1分钟

Practice homework1小时

Notes and Supplementary Material15分钟

1 个练习

Homework for Module 1.21 小时 30 分

周

周 3

完成时间为 9 小时

Module 1.3: Fourier Analysis: the Basics

The fundamental concepts behind the Fourier transform and the frequency domain

完成时间为 9 小时

14 个视频（总计 94 分钟）, 10 个阅读材料, 1 个测验

14 个视频

1.3.1.a The frequency domain5分钟

1.3.1.b The DFT as a change of basis10分钟

1.3.2.a DFT definition5分钟

1.3.2.b Examples of DFT calculation13分钟

1.3.2.c Interpreting a DFT plot4分钟

1.3.3.a DFT analysis11分钟

1.3.3.b DFT example - analysis of musical instruments4分钟

1.3.3.c DFT synthesis5分钟

1.3.3.d DFT example - tide prediction in Venice4分钟

1.3.3.e DFT example - MP3 compression4分钟

1.3.4.a The short-time Fourier transform5分钟

1.3.4.b The spectrogram7分钟

1.3.4.c Time-frequency tiling5分钟

Signal of the Day: The first man-made signal from outer space5分钟

10 个阅读材料

Introduction6分钟

What have we learned?5分钟

Introduction1分钟

What have we learned?5分钟

Introduction1分钟

What have we learned?5分钟

Introduction1分钟

What have we learned?5分钟

Practice homework1小时

Real-valued Transforms20分钟

1 个练习

Homework for Module 1.32小时

周

周 4

完成时间为 8 小时

Module 1.4: Fourier Analysis: More Advanced Tools

Delving deeper in the world of Fourier analysis.

完成时间为 8 小时

11 个视频（总计 121 分钟）, 9 个阅读材料, 1 个测验

11 个视频

1.4.1.a Discrete Fourier series4分钟

1.4.1.b Karplus-Strong revisited and DFS7分钟

1.4.2.a Karplus-Strong revisited and the DTFT13分钟

1.4.2.b Existence and properties of the DTFT7分钟

1.4.2.c The DTFT as a change of basis10分钟

1.4.3.a Sinusoidal modulation7分钟

1.4.3.b Tuning a guitar4分钟

1.4.4.a Relationships between transforms11分钟

1.4.4.b Zero padding5分钟

1.4.5 The fast Fourier transform42分钟

Signal of the Day: Tristan Chord6分钟

9 个阅读材料

Introduction1分钟

What have we learned?2分钟

Introduction1分钟

What have we learned?10分钟

Introduction5分钟

Introduction1分钟

Introduction2分钟

Practice homework1小时

Notes and Supplementary Material15分钟

1 个练习

Homework for Module 1.42小时

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来自DIGITAL SIGNAL PROCESSING 1: BASIC CONCEPTS AND ALGORITHMS的热门评论

由 AZ 提供Sep 19, 2020

Such a wonderful course, great instructors, and well structured. However, a little bit too involved and the concepts can be a little tricky to comprehend at times.

由 TS 提供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.

由 MF 提供Nov 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.

由 BL 提供Aug 22, 2020

Excellent first look into DSP, especially in the context of Fourier. Fairly math heavy so a strong math background is recommended.

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关于数字信号处理专项课程

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.

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Digital Signal Processing 1: Basic Concepts and Algorithms