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[ DevCourseWeb com ] Udemy - Signal processing problems, solved in MATLAB and in Python
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01 - Introductions
001 Signal processing = decision-making + tools.mp4
001 Signal processing = decision-making + tools_en.vtt
002 Using MATLAB in this course.mp4
002 Using MATLAB in this course_en.vtt
003 Using Octave-online in this course.mp4
003 Using Octave-online in this course_en.vtt
004 Using Python in this course.mp4
004 Using Python in this course_en.vtt
005 Having fun with filtered Glass dance.mp4
005 Having fun with filtered Glass dance_en.vtt
006 Writing code vs. using toolboxesprograms.mp4
006 Writing code vs. using toolboxesprograms_en.vtt
007 Using Udemy like a pro.mp4
007 Using Udemy like a pro_en.vtt
glassDance.mat
sigprocMXC_filterGlass.ipynb
sigprocMXC_filterGlass.m
02 - Time series denoising
001 MATLAB and Python code for this section.html
002 Mean-smooth a time series.mp4
002 Mean-smooth a time series_en.vtt
003 Gaussian-smooth a time series.mp4
003 Gaussian-smooth a time series_en.vtt
004 Gaussian-smooth a spike time series.mp4
004 Gaussian-smooth a spike time series_en.vtt
005 Denoising EMG signals via TKEO.mp4
005 Denoising EMG signals via TKEO_en.vtt
006 Median filter to remove spike noise.mp4
006 Median filter to remove spike noise_en.vtt
007 Remove linear trend (detrending).mp4
007 Remove linear trend (detrending)_en.vtt
008 Remove nonlinear trend with polynomials.mp4
008 Remove nonlinear trend with polynomials_en.vtt
009 Averaging multiple repetitions (time-synchronous averaging).mp4
009 Averaging multiple repetitions (time-synchronous averaging)_en.vtt
010 Remove artifact via least-squares template-matching.mp4
010 Remove artifact via least-squares template-matching_en.vtt
011 Code challenge Denoise these signals!.mp4
011 Code challenge Denoise these signals!_en.vtt
denoising_codeChallenge.mat
emg4TKEO.mat
eyedat.mat
sigprocMXC_GauSmoothSpikes.m
sigprocMXC_Gaussian_smooth.m
sigprocMXC_TKEO.m
sigprocMXC_averaging.m
sigprocMXC_detrend.m
sigprocMXC_mean_smooth.m
sigprocMXC_median_filter.m
sigprocMXC_polynomialDetrend.m
sigprocMXC_template_projection.m
sigprocMXC_timeSeriesDenoising.ipynb
templateProjection.mat
03 - Spectral and rhythmicity analyses
001 MATLAB and Python code for this section.html
002 Crash course on the Fourier transform.mp4
002 Crash course on the Fourier transform_en.vtt
003 Fourier transform for spectral analyses.mp4
003 Fourier transform for spectral analyses_en.vtt
004 Welch's method and windowing.mp4
004 Welch's method and windowing_en.vtt
005 Spectrogram of birdsong.mp4
005 Spectrogram of birdsong_en.vtt
006 Code challenge Compute a spectrogram!.mp4
006 Code challenge Compute a spectrogram!_en.vtt
EEGrestingState.mat
XC403881.mp3
XC403881.wav
sigprocMXC_FourierTransform.m
sigprocMXC_SpectBirdcall.m
sigprocMXC_Welch.m
sigprocMXC_spectral.ipynb
spectral_codeChallenge.mat
04 - Working with complex numbers
001 MATLAB and Python code for this section.html
002 From the number line to the complex number plane.mp4
002 From the number line to the complex number plane_en.vtt
003 Addition and subtraction with complex numbers.mp4
003 Addition and subtraction with complex numbers_en.vtt
004 Multiplication with complex numbers.mp4
004 Multiplication with complex numbers_en.vtt
005 The complex conjugate.mp4
005 The complex conjugate_en.vtt
006 Division with complex numbers.mp4
006 Division with complex numbers_en.vtt
007 Magnitude and phase of complex numbers.mp4
007 Magnitude and phase of complex numbers_en.vtt
signprocMXC_complexNumbers.ipynb
sigprocMXC_complexAddSub.m
sigprocMXC_complexConj.m
sigprocMXC_complexDivision.m
sigprocMXC_complexIntro.m
sigprocMXC_complexMult.m
sigprocMXC_complexPolar.m
05 - Filtering
001 MATLAB and Python code for this section.html
002 Filtering Intuition, goals, and types.mp4
002 Filtering Intuition, goals, and types_en.vtt
003 FIR filters with firls.mp4
003 FIR filters with firls_en.vtt
004 FIR filters with fir1.mp4
004 FIR filters with fir1_en.vtt
005 IIR Butterworth filters.mp4
005 IIR Butterworth filters_en.vtt
006 Causal and zero-phase-shift filters.mp4
006 Causal and zero-phase-shift filters_en.vtt
007 Avoid edge effects with reflection.mp4
007 Avoid edge effects with reflection_en.vtt
008 Data length and filter kernel length.mp4
008 Data length and filter kernel length_en.vtt
009 Low-pass filters.mp4
009 Low-pass filters_en.vtt
010 Windowed-sinc filters.mp4
010 Windowed-sinc filters_en.vtt
011 High-pass filters.mp4
011 High-pass filters_en.vtt
012 Narrow-band filters.mp4
012 Narrow-band filters_en.vtt
013 Two-stage wide-band filter.mp4
013 Two-stage wide-band filter_en.vtt
014 Quantifying roll-off characteristics.mp4
014 Quantifying roll-off characteristics_en.vtt
015 Remove electrical line noise and its harmonics.mp4
015 Remove electrical line noise and its harmonics_en.vtt
016 Use filtering to separate birds in a recording.mp4
016 Use filtering to separate birds in a recording_en.vtt
017 Code challenge Filter these signals!.mp4
017 Code challenge Filter these signals!_en.vtt
XC403881.mp3
XC403881.wav
filtering_codeChallenge.mat
lineNoiseData.mat
sigprocMXC_2stageWide.m
sigprocMXC_butter.m
sigprocMXC_causal0phase.m
sigprocMXC_filterTheBirds.m
sigprocMXC_filtering_part1.ipynb
sigprocMXC_filtering_part2.ipynb
sigprocMXC_fir1.m
sigprocMXC_firls.m
sigprocMXC_highpass.m
sigprocMXC_linenoise.m
sigprocMXC_lowpass.m
sigprocMXC_narrowband.m
sigprocMXC_reflection.m
sigprocMXC_rolloff.m
sigprocMXC_signalLength.m
sigprocMXC_windowSinc.m
06 - Convolution
001 MATLAB and Python code for this section.html
002 Time-domain convolution.mp4
002 Time-domain convolution_en.vtt
003 Convolution in MATLAB.mp4
003 Convolution in MATLAB_en.vtt
004 Why is the kernel flipped backwards!!!.mp4
004 Why is the kernel flipped backwards!!!_en.vtt
005 The convolution theorem.mp4
005 The convolution theorem_en.vtt
006 Thinking about convolution as spectral multiplication.mp4
006 Thinking about convolution as spectral multiplication_en.vtt
007 Convolution with time-domain Gaussian (smoothing filter).mp4
007 Convolution with time-domain Gaussian (smoothing filter)_en.vtt
008 Convolution with frequency-domain Gaussian (narrowband filter).mp4
008 Convolution with frequency-domain Gaussian (narrowband filter)_en.vtt
009 Convolution with frequency-domain Planck taper (bandpass filter).mp4
009 Convolution with frequency-domain Planck taper (bandpass filter)_en.vtt
010 Code challenge Create a frequency-domain mean-smoothing filter.mp4
010 Code challenge Create a frequency-domain mean-smoothing filter_en.vtt
sigprocMXC_FreqDomainGaus.m
sigprocMXC_TimeDomainGaus.m
sigprocMXC_convolution.ipynb
sigprocMXC_convolutionTheorem.m
sigprocMXC_planckBandPass.m
sigprocMXC_timeConvolution.m
07 - Wavelet analysis
001 MATLAB and Python code for this section.html
002 What are wavelets.mp4
002 What are wavelets_en.vtt
003 Convolution with wavelets.mp4
003 Convolution with wavelets_en.vtt
004 Scientific publication about defining Morlet wavelets.html
005 Wavelet convolution for narrowband filtering.mp4
005 Wavelet convolution for narrowband filtering_en.vtt
006 Overview Time-frequency analysis with complex wavelets.mp4
006 Overview Time-frequency analysis with complex wavelets_en.vtt
007 Link to youtube channel with 3 hours of relevant material.html
008 MATLAB Time-frequency analysis with complex wavelets.mp4
008 MATLAB Time-frequency analysis with complex wavelets_en.vtt
009 Time-frequency analysis of brain signals.mp4
009 Time-frequency analysis of brain signals_en.vtt
010 Code challenge Compare wavelet convolution and FIR filter!.mp4
010 Code challenge Compare wavelet convolution and FIR filter!_en.vtt
data4TF.mat
sigprocMXC_timefreq.m
sigprocMXC_timefreqBrain.m
sigprocMXC_wavelet.ipynb
sigprocMXC_waveletConv.m
sigprocMXC_waveletTF.m
sigprocMXC_wavelets.m
sigprocMXC_wavelets4narrowband.m
wavelet_codeChallenge.mat
08 - Resampling, interpolating, extrapolating
001 MATLAB and Python code for this section.html
002 Upsampling.mp4
002 Upsampling_en.vtt
003 Downsampling.mp4
003 Downsampling_en.vtt
004 Strategies for multirate signals.mp4
004 Strategies for multirate signals_en.vtt
005 Interpolation.mp4
005 Interpolation_en.vtt
006 Resample irregularly sampled data.mp4
006 Resample irregularly sampled data_en.vtt
007 Extrapolation.mp4
007 Extrapolation_en.vtt
008 Spectral interpolation.mp4
008 Spectral interpolation_en.vtt
009 Dynamic time warping.mp4
009 Dynamic time warping_en.vtt
010 Code challenge denoise and downsample this signal!.mp4
010 Code challenge denoise and downsample this signal!_en.vtt
resample_codeChallenge.mat
sigprocMXC_downsample.m
sigprocMXC_dtw.m
sigprocMXC_extrap.m
sigprocMXC_interp.m
sigprocMXC_irregular.m
sigprocMXC_multirate.m
sigprocMXC_resample.ipynb
sigprocMXC_spectralInterp.m
sigprocMXC_upsample.m
09 - Outlier detection
001 MATLAB and Python code for this section.html
002 Outliers via standard deviation threshold.mp4
002 Outliers via standard deviation threshold_en.vtt
003 Outliers via local threshold exceedance.mp4
003 Outliers via local threshold exceedance_en.vtt
004 Outlier time windows via sliding RMS.mp4
004 Outlier time windows via sliding RMS_en.vtt
005 Code challenge.mp4
005 Code challenge_en.vtt
forex.mat
sigprocMXC_RMSoutlierWindows.m
sigprocMXC_localOutliers.m
sigprocMXC_outZ.m
sigprocMXC_outliers.ipynb
10 - Feature detection
001 MATLAB and Python code for this section.html
002 Local maxima and minima.mp4
002 Local maxima and minima_en.vtt
003 Recover signal from noise amplitude.mp4
003 Recover signal from noise amplitude_en.vtt
004 Wavelet convolution for feature extraction.mp4
004 Wavelet convolution for feature extraction_en.vtt
005 Area under the curve.mp4
005 Area under the curve_en.vtt
006 Application Detect muscle movements from EMG recordings.mp4
006 Application Detect muscle movements from EMG recordings_en.vtt
007 Full width at half-maximum.mp4
007 Full width at half-maximum_en.vtt
008 Code challenge find the features!.mp4
008 Code challenge find the features!_en.vtt
EMGRT.mat
sigprocMXC_AUC.m
sigprocMXC_EMGonsets.m
sigprocMXC_FWHM.m
sigprocMXC_featuredetection.ipynb
sigprocMXC_localMinMax.m
sigprocMXC_signalFromNoise.m
sigprocMXC_waveletFeatureEx.m
11 - Variability
001 MATLAB and Python code for this section.html
002 Total and windowed variance and RMS.mp4
002 Total and windowed variance and RMS_en.vtt
003 Signal-to-noise ratio (SNR).mp4
003 Signal-to-noise ratio (SNR)_en.vtt
004 Coefficient of variation (CV).mp4
004 Coefficient of variation (CV)_en.vtt
005 Entropy.mp4
005 Entropy_en.vtt
006 Code challenge.mp4
006 Code challenge_en.vtt
SNRdata.mat
sigprocMXC_CV.m
sigprocMXC_SNR.m
sigprocMXC_entropy.m
sigprocMXC_variability.ipynb
sigprocMXC_windowedVar.m
v1_laminar.mat
12 - Bonus section
001 Bonus lecture.html
Bonus Resources.txt
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