Signal Processing Problems Solved in MATLAB and Python

An application-oriented guide to signal processing and digital signal processing (DSP) using MATLAB and Python code

English name of the course: Signal processing problems, solved in MATLAB and in Python

This video tutorial is 2.85GB in total, with Chinese and English subtitles, clear picture quality without watermark, and full source code attachments

Course address: https://xueshu.fun/1498 Demo address: https://www.udemy.com/course/signal-processing/

Course content

what will you learn

Learn about common signal processing tools
Design, evaluate and apply digital filters
Clean and denoise data
Know what to look for when there is a problem with data or code
Improve MATLAB or Python programming skills
Learn how to generate test signals for signal processing methods
* Complete manual correction of English subtitles!

This course includes:

12.5 hours of video on demand
13 articles
11 downloadable assets
Access on mobile and TV

Require

Basic programming experience with MATLAB or Python
High school math

describe

Why you need to study digital signal processing.

Nature is mysterious, beautiful, and complex. Trying to understand nature can be very rewarding, but also very challenging. A big challenge in studying nature is data analysis. Nature likes to mix many signal sources and many noise sources into the same recording, making your job difficult.

Therefore, one of the most important goals of time series analysis and signal processing is denoising: the separation of signal and noise mixed into the same data channel.

The big idea of DSP (Digital Signal Processing) is to discover the mysteries hidden in time series data, and this course will teach you the most common discovery strategies.

What is so special about this course?

The main focus of this course is the implementation of signal processing techniques in MATLAB and Python . Some theory and equations are shown, but I'm guessing you're reading this because you want to implement DSP techniques on real signals, not just brush up on abstract theory.

The course comes with over 10,000 lines of MATLAB and Python code, as well as example datasets that you can use to learn from and adapt to your own coursework or applications.

In this course, you will also learn how to simulate signals to test and learn more about your signal processing and analysis methods.

You will also learn how to deal with noisy or corrupted signals.

Are there prerequisites?

You need some programming experience. I've looked at videos in MATLAB, and you can also learn using Octave, a free cross-platform program that emulates MATLAB. If you like Python, I provide the corresponding Python code. You can use any other language, but you will need to do the translation yourself.

I recommend taking my course on Fourier transforms before or at the same time as this course. However, this is not required and you can successfully complete this course without taking a course on Fourier transforms.

What should you do now?

Watch the sample videos, and check out the reviews of my other courses - many of them are "bestsellers" or "top rated" and have lots of positive reviews. Feel free to message me if you are not sure if this course is right for you. I hope you see you in class!

Who this course is suitable for:

Students in Signal Processing or Digital Signal Processing (DSP) courses
Scientific or industry researchers analyzing data
Developers working with time series data
Those who want to update their filtering knowledge
Engineers who have studied DSP mathematics and want to understand software implementation

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All video tutorials include Chinese and English subtitles, exercise source code and supporting supplementary materials.