Signal processing problems, solved in MATLAB and in Python

Course Curriculum

Chapter 1: Introductions

Lecture 1: Signal processing = decision-making + tools

Lecture 2: Using MATLAB in this course

Lecture 3: Using Octave-online in this course

Lecture 4: Using Python in this course

Lecture 5: Having fun with filtered Glass dance

Lecture 6: Download all the code and data files

Lecture 7: Writing code vs. using toolboxes/programs

Lecture 8: Using Udemy like a pro

Chapter 2: Time series denoising

Lecture 1: MATLAB and Python code for this section

Lecture 2: Mean-smooth a time series

Lecture 3: Gaussian-smooth a time series

Lecture 4: Gaussian-smooth a spike time series

Lecture 5: Denoising EMG signals via TKEO

Lecture 6: Median filter to remove spike noise

Lecture 7: Remove linear trend (detrending)

Lecture 8: Remove nonlinear trend with polynomials

Lecture 9: Averaging multiple repetitions (time-synchronous averaging)

Lecture 10: Remove artifact via least-squares template-matching

Lecture 11: Code challenge: Denoise these signals!

Chapter 3: Spectral and rhythmicity analyses

Lecture 1: MATLAB and Python code for this section

Lecture 2: Crash course on the Fourier transform

Lecture 3: Fourier transform for spectral analyses

Lecture 4: Welch's method and windowing

Lecture 5: Spectrogram of birdsong

Lecture 6: Code challenge: Compute a spectrogram!

Chapter 4: Working with complex numbers

Lecture 1: MATLAB and Python code for this section

Lecture 2: From the number line to the complex number plane

Lecture 3: Addition and subtraction with complex numbers

Lecture 4: Multiplication with complex numbers

Lecture 5: The complex conjugate

Lecture 6: Division with complex numbers

Lecture 7: Magnitude and phase of complex numbers

Chapter 5: Filtering

Lecture 1: MATLAB and Python code for this section

Lecture 2: Filtering: Intuition, goals, and types

Lecture 3: FIR filters with firls

Lecture 4: FIR filters with fir1

Lecture 5: IIR Butterworth filters

Lecture 6: Causal and zero-phase-shift filters

Lecture 7: Avoid edge effects with reflection

Lecture 8: Data length and filter kernel length

Lecture 9: Low-pass filters

Lecture 10: Windowed-sinc filters

Lecture 11: High-pass filters

Lecture 12: Narrow-band filters

Lecture 13: Two-stage wide-band filter

Lecture 14: Quantifying roll-off characteristics

Lecture 15: Remove electrical line noise and its harmonics

Lecture 16: Use filtering to separate birds in a recording

Lecture 17: Code challenge: Filter these signals!

Chapter 6: Convolution

Lecture 1: MATLAB and Python code for this section

Lecture 2: Time-domain convolution

Lecture 3: Convolution in MATLAB

Lecture 4: Why is the kernel flipped backwards?!?!!?

Lecture 5: The convolution theorem

Lecture 6: Thinking about convolution as spectral multiplication

Lecture 7: Convolution with time-domain Gaussian (smoothing filter)

Lecture 8: Convolution with frequency-domain Gaussian (narrowband filter)

Lecture 9: Convolution with frequency-domain Planck taper (bandpass filter)

Lecture 10: Code challenge: Create a frequency-domain mean-smoothing filter

Chapter 7: Wavelet analysis

Lecture 1: MATLAB and Python code for this section

Lecture 2: What are wavelets?

Lecture 3: Convolution with wavelets

Lecture 4: Scientific publication about defining Morlet wavelets

Lecture 5: Wavelet convolution for narrowband filtering

Lecture 6: Overview: Time-frequency analysis with complex wavelets

Lecture 7: Link to youtube channel with 3 hours of relevant material

Lecture 8: MATLAB: Time-frequency analysis with complex wavelets

Lecture 9: Time-frequency analysis of brain signals

Lecture 10: Code challenge: Compare wavelet convolution and FIR filter!

Chapter 8: Resampling, interpolating, extrapolating

Lecture 1: MATLAB and Python code for this section

Lecture 2: Upsampling

Lecture 3: Downsampling

Lecture 4: Strategies for multirate signals

Lecture 5: Interpolation

Lecture 6: Resample irregularly sampled data

Lecture 7: Extrapolation

Lecture 8: Spectral interpolation

Lecture 9: Dynamic time warping

Lecture 10: Code challenge: denoise and downsample this signal!

Chapter 9: Outlier detection

Lecture 1: MATLAB and Python code for this section

Lecture 2: Outliers via standard deviation threshold

Lecture 3: Outliers via local threshold exceedance

Lecture 4: Outlier time windows via sliding RMS

Lecture 5: Code challenge

Chapter 10: Feature detection

Lecture 1: MATLAB and Python code for this section

Lecture 2: Local maxima and minima

Lecture 3: Recover signal from noise amplitude

Lecture 4: Wavelet convolution for feature extraction

Lecture 5: Area under the curve

Lecture 6: Application: Detect muscle movements from EMG recordings