[Udemy] - Signal processing problems, solved in MATLAB and in Python [Getnewcourses]
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| 1. Join the community!.html | 512 B | ||
| 1. MATLAB and Python code for this section.html | 102 B | ||
| 1. Signal processing = decision-making + tools.mp4 | 33.2 MB | ||
| 1. Signal processing = decision-making + tools.vtt | 5.1 KB | ||
| 1.1 sigprocMXC_TimeSeriesDenoising.zip.zip | 11.8 MB | ||
| 1.1 sigprocMXC_complex.zip.zip | 38.1 KB | ||
| 1.1 sigprocMXC_convolution.zip.zip | 250.1 KB | ||
| 1.1 sigprocMXC_featuredet.zip.zip | 1.7 MB | ||
| 1.1 sigprocMXC_filtering.zip.zip | 4.6 MB | ||
| 1.1 sigprocMXC_outliers.zip.zip | 268.3 KB | ||
| 1.1 sigprocMXC_resampling.zip.zip | 411.2 KB | ||
| 1.1 sigprocMXC_spectral.zip.zip | 2.3 MB | ||
| 1.1 sigprocMXC_variability.zip.zip | 22.2 MB | ||
| 1.1 sigprocMXC_wavelets.zip.zip | 769.7 KB | ||
| 10. Code challenge Compare wavelet convolution and FIR filter!.mp4 | 13.4 MB | ||
| 10. Code challenge Compare wavelet convolution and FIR filter!.vtt | 2.5 KB | ||
| 10. Code challenge Create a frequency-domain mean-smoothing filter.mp4 | 16.9 MB | ||
| 10. Code challenge Create a frequency-domain mean-smoothing filter.vtt | 2.1 KB | ||
| 10. Code challenge denoise and downsample this signal!.mp4 | 25.2 MB | ||
| 10. Code challenge denoise and downsample this signal!.vtt | 5 KB | ||
| 10. Remove artifact via least-squares template-matching.mp4 | 85 MB | ||
| 10. Remove artifact via least-squares template-matching.vtt | 12.3 KB | ||
| 10. Windowed-sinc filters.mp4 | 87.7 MB | ||
| 10. Windowed-sinc filters.vtt | 14.2 KB | ||
| 11. Code challenge Denoise these signals!.mp4 | 7.5 MB | ||
| 11. Code challenge Denoise these signals!.vtt | 1.3 KB | ||
| 11. High-pass filters.mp4 | 52.4 MB | ||
| 11. High-pass filters.vtt | 7.2 KB | ||
| 12. Narrow-band filters.mp4 | 55.9 MB | ||
| 12. Narrow-band filters.vtt | 7.9 KB | ||
| 13. Two-stage wide-band filter.mp4 | 42.2 MB | ||
| 13. Two-stage wide-band filter.vtt | 5.4 KB | ||
| 14. Quantifying roll-off characteristics.mp4 | 87.1 MB | ||
| 14. Quantifying roll-off characteristics.vtt | 13.3 KB | ||
| 15. Remove electrical line noise and its harmonics.mp4 | 91.1 MB | ||
| 15. Remove electrical line noise and its harmonics.vtt | 12 KB | ||
| 16. Use filtering to separate birds in a recording.mp4 | 74.7 MB | ||
| 16. Use filtering to separate birds in a recording.vtt | 7.7 KB | ||
| 17. Code challenge Filter these signals!.mp4 | 11.3 MB | ||
| 17. Code challenge Filter these signals!.vtt | 1.5 KB | ||
| 2. Bonus Coupons for related courses.html | 2.5 KB | ||
| 2. Crash course on the Fourier transform.mp4 | 116.9 MB | ||
| 2. Crash course on the Fourier transform.vtt | 18.6 KB | ||
| 2. Filtering Intuition, goals, and types.mp4 | 115.2 MB | ||
| 2. Filtering Intuition, goals, and types.vtt | 19.1 KB | ||
| 2. From the number line to the complex number plane.mp4 | 55.2 MB | ||
| 2. From the number line to the complex number plane.vtt | 12.4 KB | ||
| 2. Local maxima and minima.mp4 | 126.6 MB | ||
| 2. Local maxima and minima.vtt | 18.7 KB | ||
| 2. Mean-smooth a time series.mp4 | 66.2 MB | ||
| 2. Mean-smooth a time series.vtt | 10.2 KB | ||
| 2. Outliers via standard deviation threshold.mp4 | 69.6 MB | ||
| 2. Outliers via standard deviation threshold.vtt | 11.5 KB | ||
| 2. Time-domain convolution.mp4 | 71.1 MB | ||
| 2. Time-domain convolution.vtt | 14.7 KB | ||
| 2. Total and windowed variance and RMS.mp4 | 75.6 MB | ||
| 2. Total and windowed variance and RMS.vtt | 12.9 KB | ||
| 2. Upsampling.mp4 | 100.9 MB | ||
| 2. Upsampling.vtt | 15.8 KB | ||
| 2. Using MATLAB in this course.mp4 | 24.3 MB | ||
| 2. Using MATLAB in this course.vtt | 4.6 KB | ||
| 2. What are wavelets.mp4 | 93 MB | ||
| 2. What are wavelets.vtt | 17.4 KB | ||
| 3. Addition and subtraction with complex numbers.mp4 | 19.9 MB | ||
| 3. Addition and subtraction with complex numbers.vtt | 4.5 KB | ||
| 3. Convolution in MATLAB.mp4 | 100.7 MB | ||
| 3. Convolution in MATLAB.vtt | 15.6 KB | ||
| 3. Convolution with wavelets.mp4 | 48.2 MB | ||
| 3. Convolution with wavelets.vtt | 6.6 KB | ||
| 3. Downsampling.mp4 | 110.8 MB | ||
| 3. Downsampling.vtt | 14.8 KB | ||
| 3. FIR filters with firls.mp4 | 119.8 MB | ||
| 3. FIR filters with firls.vtt | 17.7 KB | ||
| 3. Fourier transform for spectral analyses.mp4 | 174 MB | ||
| 3. Fourier transform for spectral analyses.vtt | 23 KB | ||
| 3. Gaussian-smooth a time series.mp4 | 96.2 MB | ||
| 3. Gaussian-smooth a time series.vtt | 16.4 KB | ||
| 3. Outliers via local threshold exceedance.mp4 | 77.3 MB | ||
| 3. Outliers via local threshold exceedance.vtt | 10.7 KB | ||
| 3. Recover signal from noise amplitude.mp4 | 104.3 MB | ||
| 3. Recover signal from noise amplitude.vtt | 14.7 KB | ||
| 3. Signal-to-noise ratio (SNR).mp4 | 132.8 MB | ||
| 3. Signal-to-noise ratio (SNR).vtt | 17.8 KB | ||
| 3. Using Octave-online in this course.mp4 | 33.5 MB | ||
| 3. Using Octave-online in this course.vtt | 6.3 KB | ||
| 4. Coefficient of variation (CV).mp4 | 28.8 MB | ||
| 4. Coefficient of variation (CV).vtt | 6.1 KB | ||
| 4. FIR filters with fir1.mp4 | 47.2 MB | ||
| 4. FIR filters with fir1.vtt | 7 KB | ||
| 4. Gaussian-smooth a spike time series.mp4 | 42.2 MB | ||
| 4. Gaussian-smooth a spike time series.vtt | 6.4 KB | ||
| 4. Multiplication with complex numbers.mp4 | 39 MB | ||
| 4. Multiplication with complex numbers.vtt | 8 KB | ||
| 4. Outlier time windows via sliding RMS.mp4 | 46.1 MB | ||
| 4. Outlier time windows via sliding RMS.vtt | 7.1 KB | ||
| 4. Scientific publication about defining Morlet wavelets.html | 512 B | ||
| 4. Strategies for multirate signals.mp4 | 44.2 MB | ||
| 4. Strategies for multirate signals.vtt | 8 KB | ||
| 4. Using Python in this course.mp4 | 23.7 MB | ||
| 4. Using Python in this course.vtt | 4.4 KB | ||
| 4. Wavelet convolution for feature extraction.mp4 | 135.8 MB | ||
| 4. Wavelet convolution for feature extraction.vtt | 17.3 KB | ||
| 4. Welch's method and windowing.mp4 | 121.9 MB | ||
| 4. Welch's method and windowing.vtt | 18.5 KB | ||
| 4. Why is the kernel flipped backwards!!!.mp4 | 22.5 MB | ||
| 4. Why is the kernel flipped backwards!!!.vtt | 5.8 KB | ||
| 5. Area under the curve.mp4 | 91.2 MB | ||
| 5. Area under the curve.vtt | 15.3 KB | ||
| 5. Code challenge.mp4 | 39.1 MB | ||
| 5. Code challenge.vtt | 4.6 KB | ||
| 5. Denoising EMG signals via TKEO.mp4 | 57.2 MB | ||
| 5. Denoising EMG signals via TKEO.vtt | 9.7 KB | ||
| 5. Entropy.mp4 | 112.3 MB | ||
| 5. Entropy.vtt | 19.8 KB | ||
| 5. IIR Butterworth filters.mp4 | 80.3 MB | ||
| 5. IIR Butterworth filters.vtt | 12.4 KB | ||
| 5. Interpolation.mp4 | 55.2 MB | ||
| 5. Interpolation.vtt | 9.4 KB | ||
| 5. Spectrogram of birdsong.mp4 | 76.1 MB | ||
| 5. Spectrogram of birdsong.vtt | 9.6 KB | ||
| 5. The complex conjugate.mp4 | 23.1 MB | ||
| 5. The complex conjugate.vtt | 5.4 KB | ||
| 5. The convolution theorem.mp4 | 68.8 MB | ||
| 5. The convolution theorem.vtt | 12 KB | ||
| 5. Wavelet convolution for narrowband filtering.mp4 | 135.9 MB | ||
| 5. Wavelet convolution for narrowband filtering.vtt | 17.4 KB | ||
| 5. Writing code vs. using toolboxesprograms.mp4 | 53.1 MB | ||
| 5. Writing code vs. using toolboxesprograms.vtt | 8.5 KB | ||
| 6. Application Detect muscle movements from EMG recordings.mp4 | 151.5 MB | ||
| 6. Application Detect muscle movements from EMG recordings.vtt | 21.4 KB | ||
| 6. Causal and zero-phase-shift filters.mp4 | 82.5 MB | ||
| 6. Causal and zero-phase-shift filters.vtt | 11.9 KB | ||
| 6. Code challenge Compute a spectrogram!.mp4 | 15.2 MB | ||
| 6. Code challenge Compute a spectrogram!.vtt | 3.1 KB | ||
| 6. Code challenge.mp4 | 23.5 MB | ||
| 6. Code challenge.vtt | 3.7 KB | ||
| 6. Division with complex numbers.mp4 | 18.8 MB | ||
| 6. Division with complex numbers.vtt | 4.5 KB | ||
| 6. Median filter to remove spike noise.mp4 | 77.1 MB | ||
| 6. Median filter to remove spike noise.vtt | 12.2 KB | ||
| 6. Overview Time-frequency analysis with complex wavelets.mp4 | 48.7 MB | ||
| 6. Overview Time-frequency analysis with complex wavelets.vtt | 9.5 KB | ||
| 6. Resample irregularly sampled data.mp4 | 93.9 MB | ||
| 6. Resample irregularly sampled data.vtt | 13.2 KB | ||
| 6. Thinking about convolution as spectral multiplication.mp4 | 87.6 MB | ||
| 6. Thinking about convolution as spectral multiplication.vtt | 15.2 KB | ||
| 6. Using the Q&A forum.mp4 | 26.8 MB | ||
| 6. Using the Q&A forum.vtt | 6.4 KB | ||
| 6.1 TFtheory.mp4.mp4 | 18.2 MB | ||
| 7. Avoid edge effects with reflection.mp4 | 99.3 MB | ||
| 7. Avoid edge effects with reflection.vtt | 14 KB | ||
| 7. Convolution with time-domain Gaussian (smoothing filter).mp4 | 49.5 MB | ||
| 7. Convolution with time-domain Gaussian (smoothing filter).vtt | 7.3 KB | ||
| 7. Extrapolation.mp4 | 36.7 MB | ||
| 7. Extrapolation.vtt | 7.1 KB | ||
| 7. Full width at half-maximum.mp4 | 131.3 MB | ||
| 7. Full width at half-maximum.vtt | 21.5 KB | ||
| 7. Link to youtube channel with 3 hours of relevant material.html | 614 B | ||
| 7. Magnitude and phase of complex numbers.mp4 | 48.3 MB | ||
| 7. Magnitude and phase of complex numbers.vtt | 9.4 KB | ||
| 7. Remove linear trend (detrending).mp4 | 12.9 MB | ||
| 7. Remove linear trend (detrending).vtt | 2.6 KB | ||
| 8. Code challenge find the features!.mp4 | 24 MB | ||
| 8. Code challenge find the features!.vtt | 4.1 KB | ||
| 8. Convolution with frequency-domain Gaussian (narrowband filter).mp4 | 51.8 MB | ||
| 8. Convolution with frequency-domain Gaussian (narrowband filter).vtt | 8.1 KB | ||
| 8. Data length and filter kernel length.mp4 | 65 MB | ||
| 8. Data length and filter kernel length.vtt | 9.8 KB | ||
| 8. MATLAB Time-frequency analysis with complex wavelets.mp4 | 140.3 MB | ||
| 8. MATLAB Time-frequency analysis with complex wavelets.vtt | 17.8 KB | ||
| 8. Remove nonlinear trend with polynomials.mp4 | 109.3 MB | ||
| 8. Remove nonlinear trend with polynomials.vtt | 18.2 KB | ||
| 8. Spectral interpolation.mp4 | 77.3 MB | ||
| 8. Spectral interpolation.vtt | 12.5 KB | ||
| 9. Averaging multiple repetitions (time-synchronous averaging).mp4 | 49.7 MB | ||
| 9. Averaging multiple repetitions (time-synchronous averaging).vtt | 6.5 KB | ||
| 9. Convolution with frequency-domain Planck taper (bandpass filter).mp4 | 46.1 MB | ||
| 9. Convolution with frequency-domain Planck taper (bandpass filter).vtt | 7.5 KB | ||
| 9. Dynamic time warping.mp4 | 122.6 MB | ||
| 9. Dynamic time warping.vtt | 19.7 KB | ||
| 9. Low-pass filters.mp4 | 64 MB | ||
| 9. Low-pass filters.vtt | 8.9 KB | ||
| 9. Time-frequency analysis of brain signals.mp4 | 63.5 MB | ||
| 9. Time-frequency analysis of brain signals.vtt | 9.9 KB | ||
| ReadMe.txt | 204 B | ||
| Visit Freecourseit.com.url | 307 B | ||
| Visit Getnewcourses.com.url | 307 B | ||
| ▲ 197 total files | |||
Description
Signal processing problems, solved in MATLAB and in Python
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!
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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.
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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.
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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.
freetutorials
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
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