English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12.5 Hours | 5.70 GB
Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes
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.
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!
1 Signal processing = decision-making + tools
2 Using MATLAB in this course
3 Using Octave-online in this course
4 Using Python in this course
5 Writing code vs. using toolboxesprograms
6 Using the Q&A forum
7 MATLAB and Python code for this section
8 Local maxima and minima
9 Recover signal from noise amplitude
10 Wavelet convolution for feature extraction
11 Area under the curve
12 Application Detect muscle movements from EMG recordings
13 Full width at half-maximum
14 Code challenge find the features!
15 MATLAB and Python code for this section
16 Total and windowed variance and RMS
17 Signal-to-noise ratio (SNR)
18 Coefficient of variation (CV)
20 Code challenge
Discounts on related courses
21 Join the community!
22 Bonus Coupons for related courses
Time series denoising
23 MATLAB and Python code for this section
24 Remove artifact via least-squares template-matching
25 Code challenge Denoise these signals!
26 Mean-smooth a time series
27 Gaussian-smooth a time series
28 Gaussian-smooth a spike time series
29 Denoising EMG signals via TKEO
30 Median filter to remove spike noise
31 Remove linear trend (detrending)
32 Remove nonlinear trend with polynomials
33 Averaging multiple repetitions (time-synchronous averaging)
Spectral and rhythmicity analyses
34 MATLAB and Python code for this section
35 Crash course on the Fourier transform
36 Fourier transform for spectral analyses
37 Welch’s method and windowing
38 Spectrogram of birdsong
39 Code challenge Compute a spectrogram!
Working with complex numbers
40 MATLAB and Python code for this section
41 From the number line to the complex number plane
42 Addition and subtraction with complex numbers
43 Multiplication with complex numbers
44 The complex conjugate
45 Division with complex numbers
46 Magnitude and phase of complex numbers
47 MATLAB and Python code for this section
48 Windowed-sinc filters
49 High-pass filters
50 Narrow-band filters
51 Two-stage wide-band filter
52 Quantifying roll-off characteristics
53 Remove electrical line noise and its harmonics
54 Use filtering to separate birds in a recording
55 Code challenge Filter these signals!
56 Filtering Intuition, goals, and types
57 FIR filters with firls
58 FIR filters with fir1
59 IIR Butterworth filters
60 Causal and zero-phase-shift filters
61 Avoid edge effects with reflection
62 Data length and filter kernel length
63 Low-pass filters
64 MATLAB and Python code for this section
65 Code challenge Create a frequency-domain mean-smoothing filter
66 Time-domain convolution
67 Convolution in MATLAB
68 Why is the kernel flipped backwards!!!
69 The convolution theorem
70 Thinking about convolution as spectral multiplication
71 Convolution with time-domain Gaussian (smoothing filter)
72 Convolution with frequency-domain Gaussian (narrowband filter)
73 Convolution with frequency-domain Planck taper (bandpass filter)
74 MATLAB and Python code for this section
75 Code challenge Compare wavelet convolution and FIR filter!
76 What are wavelets
77 Convolution with wavelets
78 Scientific publication about defining Morlet wavelets
79 Wavelet convolution for narrowband filtering
80 Overview Time-frequency analysis with complex wavelets
81 Link to youtube channel with 3 hours of relevant material
82 MATLAB Time-frequency analysis with complex wavelets
83 Time-frequency analysis of brain signals
Resampling, interpolating, extrapolating
84 MATLAB and Python code for this section
85 Code challenge denoise and downsample this signal!
88 Strategies for multirate signals
90 Resample irregularly sampled data
92 Spectral interpolation
93 Dynamic time warping
94 MATLAB and Python code for this section
95 Outliers via standard deviation threshold
96 Outliers via local threshold exceedance
97 Outlier time windows via sliding RMS
98 Code challenge