An impulse takes raw data, uses signal processing to extract features, and then uses a learning
block to classify new data.
Add an input block
| Description | Recommended | |
|---|---|---|
Time series data
Operates on time series sensor data like vibration or audio data. Lets you slice up data into windows.
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Images
Processes discrete images for object detection or classification.
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Add a processing block
Did you know? You can
bring your own DSP code.
| Description | Author | Recommended | |
|---|---|---|---|
Flatten
Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.
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Edge Impulse | ||
Spectral Analysis
Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.
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Edge Impulse | ||
Spectrogram
Extracts a spectrogram from audio or sensor data, great for non-voice audio or data with continuous frequencies.
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Edge Impulse | ||
IMU (Syntiant)
Syntiant only. Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.
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Syntiant | ||
Raw Data
Use data without pre-processing. Useful if you want to use deep learning to learn features.
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Edge Impulse | ||
Flatten
Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.
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Edge Impulse | ||
Image
Preprocess and normalize image data, and optionally reduce the color depth.
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Edge Impulse | ||
Audio (MFCC)
Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.
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Edge Impulse | ||
Audio (MFE)
Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.
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Edge Impulse | ||
Spectral Analysis
Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.
|
Edge Impulse | ||
Spectrogram
Extracts a spectrogram from audio or sensor data, great for non-voice audio or data with continuous frequencies.
|
Edge Impulse | ||
Audio (Syntiant)
Syntiant only. Compute log Mel-filterbank energy features from an audio signal.
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Syntiant | ||
IMU (Syntiant)
Syntiant only. Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.
|
Syntiant | ||
Raw Data
Use data without pre-processing. Useful if you want to use deep learning to learn features.
|
Edge Impulse |
Some processing blocks have been hidden based on the data in your project.
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Add a learning block
Did you know? You can
bring your own model in PyTorch, Keras or scikit-learn.
| Description | Author | Recommended | |
|---|---|---|---|
Regression
Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.
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Edge Impulse | ||
Classification
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.
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Edge Impulse | ||
Anomaly Detection (GMM)
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Edge Impulse | ||
Anomaly Detection (K-means)
Find outliers in new data. Good for recognizing unknown states, and to complement classifiers. Works best with low dimensionality features like the output of the spectral features block.
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Edge Impulse | ||
Classification - BrainChip Akida™
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio. Only works with BrainChip AKD1000 MINI PCIe board.
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BrainChip | ||
FOMO-AD (Images)
|
Edge Impulse | ||
Regression
Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.
|
Edge Impulse | ||
Classification
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.
|
Edge Impulse | ||
Transfer Learning (Images)
Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.
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Edge Impulse | ||
Object Detection (Images)
Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets.
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Edge Impulse | ||
Transfer Learning (Keyword Spotting)
Fine tune a pre-trained keyword spotting model on your data. Good performance even with relatively small keyword datasets.
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Edge Impulse | ||
Anomaly Detection (GMM)
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Edge Impulse | ||
Anomaly Detection (K-means)
Find outliers in new data. Good for recognizing unknown states, and to complement classifiers. Works best with low dimensionality features like the output of the spectral features block.
|
Edge Impulse | ||
Classification - BrainChip Akida™
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio. Only works with BrainChip AKD1000 MINI PCIe board.
|
BrainChip | ||
Transfer Learning (Images) - BrainChip Akida™
Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets. Only works with BrainChip AKD1000 MINI PCIe board.
|
BrainChip | ||
Object Detection (Images) - BrainChip Akida™
Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets. Only works with BrainChip AKD1000 MINI PCIe board.
|
BrainChip | ||
FOMO-AD (Images)
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Edge Impulse |
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Some learning blocks have been hidden based on the data in your project.
Show all blocks anyway
Add a custom processing block
You can bring in completely custom DSP algorithms into Edge Impulse, see
See Building custom processing blocks
to get started.
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The enterprise version of Edge Impulse hosts custom blocks for you and makes
them available for your whole organization.
Try Enterprise free.
Output features
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