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30-Day Money-Back Guarantee

This course includes:

  • 12.5 hours on-demand video
  • 76 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Programming Languages Signal Processing

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
Rating: 4.4 out of 54.4 (455 ratings)
2,642 students
Created by Israel Gbati, PyTribe .
Last updated 9/2020
English
English, Afrikaans, 
30-Day Money-Back Guarantee

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

Course content

20 sections • 151 lectures • 12h 43m total length

  • Preview01:49
  • Installing Python
    01:59
  • Using IDLE
    02:52
  • Installing Python packages
    03:37
  • Testing the packages
    07:16

  • 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

  • Preview02:26
  • 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
    02:31
  • Nyquist Theorem ( Sampling Theorem )
    02:15
  • Preview05:59
  • 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

  • Preview01:17
  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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.

Featured review

Fareed Suri
Fareed Suri
47 courses
18 reviews
Rating: 4.5 out of 53 months ago
It goes into good detail with appropriate speed, but would be nice if at times we are not just seeing a ‘frozen’ screen with narration of equations going on. Also most of the lectures regarding coding are just repetition.

Instructors

Israel Gbati
Embedded Firmware Engineer
Israel Gbati
  • 4.1 Instructor Rating
  • 9,297 Reviews
  • 64,164 Students
  • 38 Courses

My name is Israel, I have been researching and working in the embedded system space for over 7 years. On Udemy I have trained  tens of thousands of students in embedded systems focusing on topics such as Assembly Programming, Real-time Operating Systems Design, Firmware Development and Digital Signal Processing. I am able to teach these topics because in my everyday work I apply concepts from these topics.

Join one of my courses and see how it goes. You can always request a refund.


PyTribe .
Practical Python Mastery for Everyone
PyTribe .
  • 4.2 Instructor Rating
  • 809 Reviews
  • 4,776 Students
  • 4 Courses

PyTribe is channel focused professional python programming.

Everyday we try to find the knowledge gaps that exist among current python developers, then put our minds and years of experience together to create a course to perfectly fill such gaps.

If you are new to python programming this channel provides step-by-step courses that will take you from beginner to expert.

If you are a python developer who wants to specialize in some specific aspects of  programming this channel provides a wide range of specialization courses to help master different aspects of programming in python.

We look forward to welcoming you to the Python-Tribe.

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