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Udemy - Signal processing problems, solved in MATLAB and in Python
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Torrent Files List
1. Introductions (Size: 5.70 GB) (Files: 197)
1. Introductions
5. Writing code vs. using toolboxesprograms.mp4
3. Using Octave-online in this course.mp4
1. Signal processing = decision-making + tools.mp4
6. Using the Q&A forum.mp4
2. Using MATLAB in this course.mp4
4. Using Python in this course.mp4
5. Writing code vs. using toolboxesprograms.vtt
6. Using the Q&A forum.vtt
3. Using Octave-online in this course.vtt
1. Signal processing = decision-making + tools.vtt
2. Using MATLAB in this course.vtt
4. Using Python in this course.vtt
ReadMe.txt
10. Feature detection
6. Application Detect muscle movements from EMG recordings.mp4
4. Wavelet convolution for feature extraction.mp4
7. Full width at half-maximum.mp4
2. Local maxima and minima.mp4
3. Recover signal from noise amplitude.mp4
5. Area under the curve.mp4
8. Code challenge find the features!.mp4
1.1 sigprocMXC_featuredet.zip.zip
7. Full width at half-maximum.vtt
6. Application Detect muscle movements from EMG recordings.vtt
2. Local maxima and minima.vtt
4. Wavelet convolution for feature extraction.vtt
5. Area under the curve.vtt
3. Recover signal from noise amplitude.vtt
8. Code challenge find the features!.vtt
1. MATLAB and Python code for this section.html
11. Variability
3. Signal-to-noise ratio (SNR).mp4
5. Entropy.mp4
2. Total and windowed variance and RMS.mp4
4. Coefficient of variation (CV).mp4
6. Code challenge.mp4
1.1 sigprocMXC_variability.zip.zip
5. Entropy.vtt
3. Signal-to-noise ratio (SNR).vtt
2. Total and windowed variance and RMS.vtt
4. Coefficient of variation (CV).vtt
6. Code challenge.vtt
1. MATLAB and Python code for this section.html
12. Discounts on related courses
2. Bonus Coupons for related courses.html
1. Join the community!.html
2. Time series denoising
8. Remove nonlinear trend with polynomials.mp4
3. Gaussian-smooth a time series.mp4
10. Remove artifact via least-squares template-matching.mp4
6. Median filter to remove spike noise.mp4
2. Mean-smooth a time series.mp4
5. Denoising EMG signals via TKEO.mp4
9. Averaging multiple repetitions (time-synchronous averaging).mp4
4. Gaussian-smooth a spike time series.mp4
7. Remove linear trend (detrending).mp4
1.1 sigprocMXC_TimeSeriesDenoising.zip.zip
11. Code challenge Denoise these signals!.mp4
8. Remove nonlinear trend with polynomials.vtt
3. Gaussian-smooth a time series.vtt
10. Remove artifact via least-squares template-matching.vtt
6. Median filter to remove spike noise.vtt
2. Mean-smooth a time series.vtt
5. Denoising EMG signals via TKEO.vtt
9. Averaging multiple repetitions (time-synchronous averaging).vtt
4. Gaussian-smooth a spike time series.vtt
7. Remove linear trend (detrending).vtt
11. Code challenge Denoise these signals!.vtt
1. MATLAB and Python code for this section.html
3. Spectral and rhythmicity analyses
3. Fourier transform for spectral analyses.mp4
4. Welch's method and windowing.mp4
2. Crash course on the Fourier transform.mp4
5. Spectrogram of birdsong.mp4
6. Code challenge Compute a spectrogram!.mp4
1.1 sigprocMXC_spectral.zip.zip
3. Fourier transform for spectral analyses.vtt
2. Crash course on the Fourier transform.vtt
4. Welch's method and windowing.vtt
5. Spectrogram of birdsong.vtt
6. Code challenge Compute a spectrogram!.vtt
1. MATLAB and Python code for this section.html
4. Working with complex numbers
2. From the number line to the complex number plane.mp4
7. Magnitude and phase of complex numbers.mp4
4. Multiplication with complex numbers.mp4
5. The complex conjugate.mp4
3. Addition and subtraction with complex numbers.mp4
6. Division with complex numbers.mp4
1.1 sigprocMXC_complex.zip.zip
2. From the number line to the complex number plane.vtt
7. Magnitude and phase of complex numbers.vtt
4. Multiplication with complex numbers.vtt
5. The complex conjugate.vtt
6. Division with complex numbers.vtt
3. Addition and subtraction with complex numbers.vtt
1. MATLAB and Python code for this section.html
5. Filtering
3. FIR filters with firls.mp4
2. Filtering Intuition, goals, and types.mp4
7. Avoid edge effects with reflection.mp4
15. Remove electrical line noise and its harmonics.mp4
10. Windowed-sinc filters.mp4
14. Quantifying roll-off characteristics.mp4
6. Causal and zero-phase-shift filters.mp4
5. IIR Butterworth filters.mp4
16. Use filtering to separate birds in a recording.mp4
8. Data length and filter kernel length.mp4
9. Low-pass filters.mp4
12. Narrow-band filters.mp4
11. High-pass filters.mp4
4. FIR filters with fir1.mp4
13. Two-stage wide-band filter.mp4
17. Code challenge Filter these signals!.mp4
1.1 sigprocMXC_filtering.zip.zip
2. Filtering Intuition, goals, and types.vtt
3. FIR filters with firls.vtt
10. Windowed-sinc filters.vtt
7. Avoid edge effects with reflection.vtt
14. Quantifying roll-off characteristics.vtt
5. IIR Butterworth filters.vtt
15. Remove electrical line noise and its harmonics.vtt
6. Causal and zero-phase-shift filters.vtt
8. Data length and filter kernel length.vtt
9. Low-pass filters.vtt
12. Narrow-band filters.vtt
16. Use filtering to separate birds in a recording.vtt
11. High-pass filters.vtt
4. FIR filters with fir1.vtt
13. Two-stage wide-band filter.vtt
17. Code challenge Filter these signals!.vtt
1. MATLAB and Python code for this section.html
6. Convolution
3. Convolution in MATLAB.mp4
6. Thinking about convolution as spectral multiplication.mp4
2. Time-domain convolution.mp4
5. The convolution theorem.mp4
8. Convolution with frequency-domain Gaussian (narrowband filter).mp4
7. Convolution with time-domain Gaussian (smoothing filter).mp4
9. Convolution with frequency-domain Planck taper (bandpass filter).mp4
4. Why is the kernel flipped backwards!!!.mp4
6.1 TFtheory.mp4.mp4
10. Code challenge Create a frequency-domain mean-smoothing filter.mp4
1.1 sigprocMXC_convolution.zip.zip
3. Convolution in MATLAB.vtt
6. Thinking about convolution as spectral multiplication.vtt
2. Time-domain convolution.vtt
5. The convolution theorem.vtt
8. Convolution with frequency-domain Gaussian (narrowband filter).vtt
9. Convolution with frequency-domain Planck taper (bandpass filter).vtt
7. Convolution with time-domain Gaussian (smoothing filter).vtt
4. Why is the kernel flipped backwards!!!.vtt
10. Code challenge Create a frequency-domain mean-smoothing filter.vtt
1. MATLAB and Python code for this section.html
7. Wavelet analysis
8. MATLAB Time-frequency analysis with complex wavelets.mp4
5. Wavelet convolution for narrowband filtering.mp4
2. What are wavelets.mp4
9. Time-frequency analysis of brain signals.mp4
6. Overview Time-frequency analysis with complex wavelets.mp4
3. Convolution with wavelets.mp4
10. Code challenge Compare wavelet convolution and FIR filter!.mp4
1.1 sigprocMXC_wavelets.zip.zip
8. MATLAB Time-frequency analysis with complex wavelets.vtt
2. What are wavelets.vtt
5. Wavelet convolution for narrowband filtering.vtt
9. Time-frequency analysis of brain signals.vtt
6. Overview Time-frequency analysis with complex wavelets.vtt
3. Convolution with wavelets.vtt
10. Code challenge Compare wavelet convolution and FIR filter!.vtt
7. Link to youtube channel with 3 hours of relevant material.html
4. Scientific publication about defining Morlet wavelets.html
1. MATLAB and Python code for this section.html
8. Resampling, interpolating, extrapolating
9. Dynamic time warping.mp4
3. Downsampling.mp4
2. Upsampling.mp4
6. Resample irregularly sampled data.mp4
8. Spectral interpolation.mp4
5. Interpolation.mp4
4. Strategies for multirate signals.mp4
7. Extrapolation.mp4
10. Code challenge denoise and downsample this signal!.mp4
1.1 sigprocMXC_resampling.zip.zip
9. Dynamic time warping.vtt
2. Upsampling.vtt
3. Downsampling.vtt
6. Resample irregularly sampled data.vtt
8. Spectral interpolation.vtt
5. Interpolation.vtt
4. Strategies for multirate signals.vtt
7. Extrapolation.vtt
10. Code challenge denoise and downsample this signal!.vtt
1. MATLAB and Python code for this section.html
9. Outlier detection
3. Outliers via local threshold exceedance.mp4
2. Outliers via standard deviation threshold.mp4
4. Outlier time windows via sliding RMS.mp4
5. Code challenge.mp4
1.1 sigprocMXC_outliers.zip.zip
2. Outliers via standard deviation threshold.vtt
3. Outliers via local threshold exceedance.vtt
4. Outlier time windows via sliding RMS.vtt
5. Code challenge.vtt
1. MATLAB and Python code for this section.html
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ReadMe.txt
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