Mô tả

When I was an undergraduate I took a course called Linear Systems, which provides background theory for courses like Digital Signal Processing, Control Systems, and Communication Systems. While I did earn a grade of A in the course, I never really understood the purpose of the course beyond it being a prerequisite to other courses that I was required to take.

My goal in this course is to introduce you to digital signal processing in such a way that you not only understand the purpose of the various topics, but that you also see how you can apply the material.

In order to demonstrate practical applications of digital signal processing, I provide about a dozen Python programs for doing such things as removing noise from audio files, removing noise from images, identifying which phone numbers are pressed on a touch-tone phone, and analyzing temperature data. I go over each program, explaining how it works and how I designed it. I don't assume that you have already programmed using the Python programming language, so I also provide a crash course to get you up to speed.

This course is not for someone wanting a rigorous, theory- and math-heavy course; there are many available options if this is what you are looking for.  This isn't to say that we will not use math in this course. I think that there is too much that you need to know that you can't really understand without some math.  To help you with the math that we will learn, I review complex numbers and complex exponentials at the beginning of the course. Then as we learn new topics I provide practice problems with my solved answers.

Bạn sẽ học được gì

How signals are represented by sinusoids.

What it means for a system to be linear and time-invariant.

How digital filters can be represented by difference equations.

What the frequency response of a system is.

What convolution is and why it is important in signal processing.

What it means for two signals to be correlated.

How the discrete Fourier transform can be used to identify the frequencies present in a signal.

Get a crash course in Python.

How Python can be used to produce practical applications of digital signal processing.

Yêu cầu

  • It would be nice to have had linear algebra, but most of what is taught can be understood without it.
  • If you wish to run the code, then you will need a computer that can run Python.
  • Python 3.x (directions for installing are given in the course).

Nội dung khoá học

13 sections

Introduction

3 lectures
Introduction
15:45
Review of Complex Numbers
11:19
Review of Complex Arithmetic (with practice problems)
11:11

Python Crash Course

14 lectures
Installing Anaconda on Linux (also watch if using Mac OS)
13:36
Installing Anaconda on Windows
07:41
Statements
18:45
Booleans
10:02
Conditionals
22:53
Loops
08:32
Program Development
23:47
Functions
14:36
Lists
20:29
Strings
12:39
Files
12:07
Dictionaries
15:09
Numpy
17:13
Matplotlib
11:13

Sinusoids and Basic Signals

8 lectures
Sinusoids
13:59
Sinusoids Example (with practice problems)
07:34
Sampling
09:56
Aliasing (with practice problems)
12:01
Application: Music Generation
17:20
Basic Filters
06:42
Basic Signals
03:28
Difference Equations (with practice problems)
12:47

Linear, Time-Invariant (LTI) Systems

5 lectures
Linear, Time-Invariant (LTI) Systems
14:35
Linearity Examples, part 1
13:32
Linearity Examples, part 2 (with practice problems)
13:07
Time-Invariance Examples
10:36
Application: Decoding a Digital Message
21:09

Time-Domain Analysis

9 lectures
Impulse Response
18:32
FIR vs IIR Filters
11:24
Linear Convolution
15:48
Convolution Property: Commutativity
03:16
Convolution Property: Associativity
05:37
Convolution Property: Distributitvity (with practice problems)
07:06
Application: Image Processing
25:30
Correlation (with practice problems)
25:30
Application: Template Matching
12:23

Frequency-Domain Analysis

2 lectures
Frequency-Domain Analysis
15:28
Harmonics (with practice problems)
07:46

Discrete Fourier Transform

4 lectures
The Discrete Fourier Transform (DFT)
17:29
DFT: A Conceptual Understanding (with practice problems)
28:52
Application: Noise Removal from Audio using the DFT
11:49
Application: Analyzing Temperature Data using the DFT
18:32

Frequency Response

2 lectures
Frequency Response of a Filter
21:15
Frequency Response and Convolution
03:57

Spectrogram

4 lectures
The Spectrogram
18:03
Application: Identifying a Phone Number using DTMF
15:54
Feature Selection
05:48
Application: Classifying Audio Files
10:32

Design of Nonrecursive Filters

3 lectures
Design of Nonrecursive Filters, part 1
24:01
Design of Nonrecursive Filters, part 2
05:23
Application: Noise Removal from Audio using an FIR Filter
05:05

Frequency-Domain Analysis and the z-Transform

5 lectures
The z-Transform
13:11
The z-Transform: Poles and Zeros
06:23
The z-Transform: Examples
10:24
The z-Transform and Convolution (with practice problems)
04:14
Application: Remove a Specific Frequency with a Notch Filter
07:39

Design of Recursive Filters

4 lectures
Design of Recursive Filters, part 1
09:44
Design of Recursive Filters, part 2 (with practice problems)
06:37
Application: Change Low Frequencies with a Shelving Filter
08:47
Application: Separate Audio with Blind Source Separation
10:15

End of Course

1 lectures
Where to Go From Here
09:39

Đánh giá của học viên

Chưa có đánh giá
Course Rating
5
0%
4
0%
3
0%
2
0%
1
0%

Bình luận khách hàng

Viết Bình Luận

Bạn đánh giá khoá học này thế nào?

image

Đăng ký get khoá học Udemy - Unica - Gitiho giá chỉ 50k!

Get khoá học giá rẻ ngay trước khi bị fix.