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Signal data cleaner
Give it raw signals. Get gaps filled, drift removed, noise cut and labels added for analysis.
Signal data cleaner is a free AI skill for the job "Build and ship software". Give the instructions to an AI assistant that supports the required files and tools, then review its output.
Free · No account · BSD 2 Clause licence · Made by MathWorks
Made by MathWorks. See the original on GitHub; we copied it as it is, with its licence.
What it does
A MATLAB script that turns a raw signal into clean, labelled data ready for analysis. It saves you from inventing filters that are wrong or functions that do not exist. The skill follows one order: condition the signal, load files into a datastore, label, split and frame, then hand off to training. For cleaning, it uses the standard tools: fill short gaps with interpolation, fill long gaps in oscillating signals with an AR method, and remove slow drift with a polynomial detrend, raising the order step by step. It also handles outliers and noise. It needs MATLAB with the Signal Processing Toolbox.
You give it
- Your signal data or a folder of files
- Sample rate
- What problems you see: gaps, drift, noise
- MATLAB with Signal Processing Toolbox
You get
- A runnable .m cleaning script
- Gaps filled and drift removed
- Outlier handling
- Labelled, split data for training
Use it when
- Your sensor data has holes and spikes before you can analyse it.
- The signal drifts slowly and hides what you want to see.
- You need to label recordings for machine learning.
- You have a folder of signal files and need one clean dataset.
How to use it
- Add it to your AI once: download it and add the folder to Claude, ChatGPT or Codex as a skill.
- Ask for the job in your own words. Your AI opens the skill when it fits.
- It works best when your AI can run MATLAB with Signal Processing Toolbox.