Create impulse - Tutorial: Adding sight to your sensors

An impulse takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.

Description	Author	Recommended
Images Processes discrete images for object detection or classification.	EdgeImpulse Inc.
Time series data Operates on time series sensor data like vibration or audio data. Lets you slice up data into windows.	EdgeImpulse Inc.

Description	Author	Recommended
Image Preprocess and normalize image data, and optionally reduce the color depth.	EdgeImpulse Inc.
Flatten Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.	EdgeImpulse Inc.
Audio (MFCC) Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	EdgeImpulse Inc.
Audio (MFE) Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.	EdgeImpulse Inc.
Spectral Analysis Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	EdgeImpulse Inc.
Spectrogram Extracts a spectrogram from audio or sensor data, great for non-voice audio or data with continuous frequencies.	EdgeImpulse Inc.
Audio (Syntiant) Experimental Syntiant only. Compute log Mel-filterbank energy features from an audio signal.	EdgeImpulse Inc.
Raw Data Use data without pre-processing. Useful if you want to use deep learning to learn features.	EdgeImpulse Inc.
Spectral features v2 Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	Edge Impulse Inc.
Syntiant IMU Raw Block to use with Syntiant IMU	Edge Impulse Inc.
IMF (Iterative Filtering) Extract IMFs from a signal using iterative filtering	Edge Impulse Inc.

Some learning blocks have been hidden based on the data in your project.

Description	Author	Recommended
Transfer Learning (Images) Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.	EdgeImpulse Inc.
Classification (Keras) Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	EdgeImpulse Inc.
Regression (Keras) Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	EdgeImpulse Inc.

Data type

Input axes

This block does not have any other settings

Image width

Image height

Resize mode

Resize method

For optimal accuracy with transfer learning blocks, use a 96x96 or 160x160 image size.

Window size

Window increase

Frequency (Hz)

Invalid frequency

Zero-pad data

Name

Input features

Output features

4 (idle, snake, updown, wave)

This block only works with a window size of 1000ms at 16000Hz, and an Audio (MFE) block with default parameters.