What is the best way to use labels from the Signal Labeler application?

Hello, I have many csv files containing signal data. For example an input voltage sweep (x-axis) and the coresponding output voltage(y-axis). This data is often non linear. My task is to label the data by mean slope variance etc.. for use in training a ML model. My plan so far is to use the data labeler to label specific Regions of Interest (ROI) on the signals, then export these into the matlab workspace and use them to split the signal and work out values for each section. This approach seems tedius. What is a more optimal workflow for this action? I am also aware that regions can be autolabeled with custom functions, but I want to get the basic workflow down first before I move on to this.

Answer 1

[Rijuta]

Hi Ian,

I understand that you have a dataset of signal data in CSV files, and you want to label the data based on various statistics for use in training a machine learning model.

Please follow the following steps to use MATLAB functions and make the task less tedious.

• Instead of using the data labeller, you can directly load the CSV files into MATLAB using the ‘csvread’ function.
• Use the ‘plot’ function to visualize the data and identify any regions of interest.
• Use MATLAB's built-in functions to calculate the desired statistics.
• Export the labelled data to a new CSV file or save it as a MATLAB variable for use in training your machine learning model.

I hope the above steps help you streamline the workflow and avoid the tedium of manually labeling regions of interest in the data labeller.