Code for Webinar "Signal Processing for Machine Learning"
Updated 1 Sep 2016
These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used.
In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone.
We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset.
The topics discussed include:
* Signal manipulation and visualisation
* Design and application of digital filters
* Frequency-domain analysis
* Automatic peak detection
* Feature extraction from signals
* Train and test of simple Neural Networks
Gabriele Bunkheila (2023). Code for Webinar "Signal Processing for Machine Learning" (https://www.mathworks.com/matlabcentral/fileexchange/49893-code-for-webinar-signal-processing-for-machine-learning), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Classification >
- Signal Processing > Signal Processing Toolbox > Measurements and Feature Extraction >
- AI, Data Science, and Statistics > Deep Learning Toolbox > Sequence and Numeric Feature Data Workflows >
Inspired by: sloc
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