Processing blocks
Extracting meaningful features from your data is crucial to building small and reliable machine learning models, and in Edge Impulse this is done through processing blocks. We ship a number of processing blocks for common sensor data (such as vibration and audio):
The source code of these blocks are available in the Edge Impulse processing blocks GitHub repository.
Custom processing blocks
If you have a very specific sensor, want to apply custom filters, or are implementing the latest research in digital signal processing, follow our tutorial on Building custom processing blocks.
Feature importance
In most of our DSP blocks, you have the option to calculate the feature importance. Edge Impulse Studio will then output a Feature Importance list that will help you determine which axes generated from your DSP block are most significant to analyze when you want to train a model.
Feature importance
For feature importance to work, you must have at least two labeled classes in your training dataset
This process of generating features and determining the most important features of your data will further reduce the amount of signal analysis needed on the device with new and unseen data.
To calculate the feature importance, a RandomForestClassifier is trained on the data and the feature_importances_ are extracted from the trained classifier.
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