The Raw Data block generates windows from data samples without any specific signal processing. It is great for signals that have already been pre-processed and if you just need to feed your data into the Neural Network block. GitHub repository containing all DSP block code: edgeimpulse/processing-blocks.

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Raw data parameters

Normalize features To enable data normalization, you can use the Normalize features option in the processing blocks generate features tab. This will learn the mean and standard deviation of each output column during the feature generation step, and apply a normalization step during training and inference.

Normalize features

When enabled, the block learns the mean and standard deviation of every output column during Generate features, then applies a normalization step at training and inference. Recommended when raw numeric ranges differ by more than ~10×. see Why normalize? for more details. Scaling
  • Scale axes: Multiplies each axis by this number. This can be used to normalize your data between 0 and 1.

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How does the raw data block work?

The Raw Data block retrieves raw samples and applies the Scaling parameter.