Digital Signal Processing (DSP) From Ground Up™ in Python

2,023 students enrolled

Digital Signal Processing (DSP) From Ground Up™ in Python

Practical DSP in Python : Over 70 examples, FFT,Filter Design, IIR,FIR, Window Filters,Convolution,Linear Systems etc

2,023 students enrolled

Created by Israel Gbati, PyTribe .

Last updated 4/2020

English

English, Afrikaans,

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Digital Signal Processing (DSP) From Ground Up™ in Python

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12.5 hours on-demand video
76 downloadable resources
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What you'll learn

Develop the Convolution Kernel algorithm in Python
Design and develop 17 different window filters in Python
Develop the Discrete Fourier Transform (DFT) algorithm in Python
Design and develop Type I Chebyshev filters in Python
Design and develop Type II Chebyshev filters in Python
Develop the Inverse Discrete Fourier Transform (IDFT) algorithm in Pyhton
Develop the Fast Fourier Transform (FFT) algorithm in Python
Perform spectral analysis on ECG signals in Python
Design and develop Windowed-Sinc filters in Python
Design and develop Finite Impulse Response (FIR) filters in Python
Design and develop Infinite Impulse Response (IIR) filters in Python
Develop the First Difference algorithm in Python
Develop the Running Sum algorithm in Python
Develop the Moving Average filter algorithm in Python
Develop the Recursive Moving Average filter algorithm in Python
Design and develop Butterworth filters in Python
Design and develop Match filters in Python
Design and develop Bessel filters in Python
Simulate Linear Time Invariant (LTI) Systems in Python
Perform linear and cubic interpolation in Python

5 lectures 17:33

Downloading Python

Preview 01:49

Installing Python

01:59

Using IDLE

02:52

Installing Python packages

03:37

Testing the packages

07:16

+ – Python Essentials

10 lectures 01:22:37

Printing statements

06:06

Variables

07:19

Lists

05:17

Operators

12:23

Conditions

08:14

For Loops

07:28

While Loops

05:26

Functions

07:44

Dictionaries

10:17

Classes and Objects

12:23

+ – Signal Statistics and Noise

12 lectures 01:04:51

Nature of a signal

Preview 02:26

Coding : Plotting signals with pyplot

07:35

Coding : Importing signals and dealing with subplots

12:31

Coding : Generating signals

12:07

Mean and Standard Deviation

04:10

Coding : Computing the Signal Mean

04:42

Coding : Developing the Signal Mean algorithm

05:33

Coding : Computing the Signal Variance

01:57

Coding : Developing the Signal Variance algorithm

07:27

Coding : Computing the Standard Deviation

02:19

Coding : Developing the Signal Standard Deviation algorithm

03:06

Signal-to-Noise ratio

00:58

+ – Quantization and The Sampling Theorem

8 lectures 23:04

Quantization

02:31

Nyquist Theorem ( Sampling Theorem )

02:15

The Passive Low-Pass Filter

Preview 05:59

The Passive High-Pass Filter

02:29

The Modified Sallen-Key Filter

02:19

The Bessel, Chebyshev and Butterworth filters

03:28

Comparing the performance of the Bessel, Chebyshev and Butterworth filters

02:36

Information encoding : Time-domain and frequency-domain encoding

01:27

+ – Linear Systems and Superposition

7 lectures 10:40

Signal naming conventions

Preview 01:17

System Homogeneity

01:32

System Additivity

00:33

System Shift Invariance

01:06

Synthesis and Decomposition

02:11

Impulse Decomposition

02:10

Step Decomposition

01:51

+ – Convolution

16 lectures 01:33:41

Introduction to Convolution

01:33

The Delta Function and Impulse Response

02:13

The Convolution Kernel

07:22

The Convolution Kernel (Part II)

00:53

The Output side analysis and the convolution sum equation

04:20

Coding : Examining the signals

07:19

Coding : Computing the convolution of two signals

07:07

Coding : Developing the Convolution algorithm

21:11

Coding : Computing the De-convolution of two signals

06:19

Coding : Correlation

07:17

The Identity property of convolution

01:30

The Running Sum and First Difference

02:02

Coding : Computing the running sum of a signal

08:04

Coding : Developing the Running Sum algorithm

06:52

Coding : Computing the First Difference of a signal

03:12

Coding : Developing the First Difference algorithm

06:27

+ – Fourier Transsform

13 lectures 01:18:28

Introduction to Fourier Analysis

01:22

Introduction to Discrete Fourier Transform

04:50

DFT Basis Functions

03:23

Deducing the Inverse DFT

03:12

Calculating the Discrete Fourier Transform (DFT)

03:55

Coding : Developing the Discrete Fourier Transform (DFT) algorithm

15:53

Coding : Developing the DFT magnitude algorithm

10:03

Coding : Developing the Inverse Discrete Fourier Transform (IDFT) algorithm

20:35

Coding : Computing the IDFT of an ECG signal

05:25

Symmetry between Time domain and frequency domain -Duality

00:55

Polar Notation

02:50

Introduction to Spectral Analysis

02:31

The Frequency Response

03:34

+ – Complex Numbers

5 lectures 08:46

The Complex Number System

02:05

Polar Representation of Complex Numbers

01:35

Euler's Relation

01:35

Representation of Sinusoids

01:57

Representing Systems

01:34

+ – Complex Fourier Transform

4 lectures 07:14

Introduction to Complex Fourier Transform

01:43

Mathematical Equivalence

01:38

The Complex DFT Equation

00:36

Comparing Real DFT and Complex DFT

03:17

+ – Fast Fourier Transform (FFT)

4 lectures 31:43

An Overview of how FFT works.

08:17

Understanding the complexity of calculating DFT directly

02:35

How the Decimation -in-Time FFT Algorithm works

09:00

Coding : Computing the FFT of a signal

11:51

10 more sections

Requirements

You will need just a good working computer for this course

Description

With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Digital Signal Processing (DSP) in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding obstacles of abstract mathematical theories. To achieve this goal, the DSP techniques are explained in plain language, not simply proven to be true through mathematical derivations.

Still keeping it simple, this course comes in different programming languages and hardware architectures so that students can put the techniques to practice using a programming language or hardware architecture of their choice. This version of the course uses the Python programming language.

By the end of this course you should be able develop the Convolution Kernel algorithm in python, develop 17 different types of window filters in python, develop the Discrete Fourier Transform (DFT) algorithm in python, develop the Inverse Discrete Fourier Transform (IDFT) algorithm in pyhton, design and develop Finite Impulse Response (FIR) filters in python, design and develop Infinite Impulse Response (IIR) filters in python, develop Type I Chebyshev filters in python, develop Type II Chebyshev filters in python, perform spectral analysis on ECG signals in python, develop Butterworth filters in python, develop Match filters in python,simulate Linear Time Invariant (LTI) Systems in python, even give a lecture on DSP and so much more. Please take a look at the full course curriculum.

Who this course is for:

People working in the field of signal processing
University students taking classes in signal processing
Python developers who wish to expand their skills
People who want to understand signal processing practically and apply it to their respective fields.