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2020-11-30 12:24:58
30-Day Money-Back Guarantee

This course includes:

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

Signal processing problems, solved in MATLAB and in Python

Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes
Bestseller
Rating: 4.6 out of 54.6 (1,120 ratings)
7,016 students
Created by Mike X Cohen
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Understand commonly used signal processing tools
  • Design, evaluate, and apply digital filters
  • Clean and denoise data
  • Know what to look for when something isn't right with the data or the code
  • Improve MATLAB or Python programming skills
  • Know how to generate test signals for signal processing methods
  • *Fully manually corrected English captions!
Curated for the Udemy for Business collection

Course content

12 sections • 98 lectures • 12h 32m total length

  • Preview04:07
  • Using MATLAB in this course
    03:34
  • Using Octave-online in this course
    04:52
  • Using Python in this course
    03:32
  • Having fun with filtered Glass dance
    07:48
  • Writing code vs. using toolboxes/programs
    06:53
  • Using Udemy like a pro
    07:57

  • MATLAB and Python code for this section
    00:03
  • Preview08:15
  • Gaussian-smooth a time series
    12:35
  • Gaussian-smooth a spike time series
    05:10
  • Preview08:00
  • Median filter to remove spike noise
    09:53
  • Remove linear trend (detrending)
    02:09
  • Remove nonlinear trend with polynomials
    14:35
  • Averaging multiple repetitions (time-synchronous averaging)
    05:19
  • Remove artifact via least-squares template-matching
    10:34
  • Code challenge: Denoise these signals!
    01:07

  • MATLAB and Python code for this section
    00:03
  • Crash course on the Fourier transform
    15:17
  • Fourier transform for spectral analyses
    18:46
  • Welch's method and windowing
    15:28
  • Spectrogram of birdsong
    08:21
  • Code challenge: Compute a spectrogram!
    02:32

  • MATLAB and Python code for this section
    00:01
  • From the number line to the complex number plane
    10:15
  • Addition and subtraction with complex numbers
    03:31
  • Multiplication with complex numbers
    06:17
  • The complex conjugate
    04:21
  • Division with complex numbers
    03:52
  • Magnitude and phase of complex numbers
    07:50

  • MATLAB and Python code for this section
    00:03
  • Filtering: Intuition, goals, and types
    16:59
  • FIR filters with firls
    14:46
  • FIR filters with fir1
    06:16
  • IIR Butterworth filters
    10:13
  • Causal and zero-phase-shift filters
    09:32
  • Preview11:40
  • Data length and filter kernel length
    07:58
  • Low-pass filters
    07:08
  • Windowed-sinc filters
    12:01
  • High-pass filters
    06:07
  • Narrow-band filters
    06:41
  • Preview04:50
  • Quantifying roll-off characteristics
    11:39
  • Remove electrical line noise and its harmonics
    10:10
  • Use filtering to separate birds in a recording
    07:03
  • Code challenge: Filter these signals!
    01:25

  • MATLAB and Python code for this section
    00:02
  • Time-domain convolution
    11:51
  • Convolution in MATLAB
    11:47
  • Why is the kernel flipped backwards?!?!!?
    04:43
  • The convolution theorem
    09:57
  • Thinking about convolution as spectral multiplication
    12:20
  • Convolution with time-domain Gaussian (smoothing filter)
    06:00
  • Convolution with frequency-domain Gaussian (narrowband filter)
    07:05
  • Convolution with frequency-domain Planck taper (bandpass filter)
    06:13
  • Code challenge: Create a frequency-domain mean-smoothing filter
    01:42

  • MATLAB and Python code for this section
    00:03
  • What are wavelets?
    12:55
  • Convolution with wavelets
    05:15
  • Scientific publication about defining Morlet wavelets
    00:10
  • Wavelet convolution for narrowband filtering
    14:45
  • Overview: Time-frequency analysis with complex wavelets
    08:00
  • Link to youtube channel with 3 hours of relevant material
    00:14
  • MATLAB: Time-frequency analysis with complex wavelets
    14:53
  • Time-frequency analysis of brain signals
    07:59
  • Code challenge: Compare wavelet convolution and FIR filter!
    02:01

  • MATLAB and Python code for this section
    00:02
  • Upsampling
    13:08
  • Downsampling
    12:38
  • Preview06:39
  • Interpolation
    07:37
  • Resample irregularly sampled data
    11:14
  • Extrapolation
    06:04
  • Spectral interpolation
    10:18
  • Dynamic time warping
    16:12
  • Code challenge: denoise and downsample this signal!
    04:16

  • MATLAB and Python code for this section
    00:02
  • Outliers via standard deviation threshold
    08:56
  • Outliers via local threshold exceedance
    08:34
  • Outlier time windows via sliding RMS
    05:51
  • Code challenge
    03:53

  • MATLAB and Python code for this section
    00:02
  • Local maxima and minima
    14:41
  • Recover signal from noise amplitude
    11:38
  • Wavelet convolution for feature extraction
    13:31
  • Area under the curve
    12:48
  • Application: Detect muscle movements from EMG recordings
    17:45
  • Full width at half-maximum
    17:00
  • Code challenge: find the features!
    03:13

Requirements

  • Basic programming experience in MATLAB or Python
  • High-school math

Description

Why you need to learn digital signal processing.

Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources of noise into the same recordings, and this makes your job difficult.

Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noises that are mixed into the same data channels.

The big idea of DSP (digital signal processing) is to discover the mysteries that are hidden inside time series data, and this course will teach you the most commonly used discovery strategies.


What's special about this course?

The main focus of this course is on implementing signal processing techniques in MATLAB and in Python. Some theory and equations are shown, but I'm guessing you are 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, plus sample data sets, which you can use to learn from and to adapt to your own coursework or applications.

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

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


Are there prerequisites?

You need some programming experience. I go through the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide corresponding Python code if you prefer Python. You can use any other language, but you would need to do the translation yourself.

I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement, and you can succeed in this course without taking the Fourier transform course.


What should you do now?

Watch the sample videos, and check out the reviews of my other courses -- many of them are "best-seller" or "top-rated" and have lots of positive reviews. If you are unsure whether this course is right for you, then feel free to send me a message. I hope you to see you in class!

Who this course is for:

  • Students in a signal processing or digital signal processing (DSP) course
  • Scientific or industry researchers who analyze data
  • Developers who work with time series data
  • Someone who wants to refresh their knowledge about filtering
  • Engineers who learned the math of DSP and want to learn about implementations in software

Featured review

Md Junayed Hasan
Md Junayed Hasan
13 courses
4 reviews
Rating: 5.0 out of 57 months ago
Professor MIKE is awesome. He covers everything in a such a way, so that the students get motivations to learn more and more. Thank you Professor. A request to the Professor from me: Please make some tutorials / video lectures about "How to detect whether a signal is stationary or not , and linear or not? "

Instructor

Mike X Cohen
Neuroscientist, writer, professor
Mike X Cohen
  • 4.5 Instructor Rating
  • 20,520 Reviews
  • 103,463 Students
  • 20 Courses

I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.

But you're here because of my teaching, so let me tell you about that: 

I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.

I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.

Over 94,000 students have watched over 6,500,000 minutes of my courses (that's over 12 years of continuous learning). Come find out why!

I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)

                                                  -------------------------

By popular request, here are suggested course progressions for various educational goals:

MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing

Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python

Applied linear algebra: Complete Linear Algebra; Dimension Reduction

Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

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