TI-ML-Audio-Classification - Create impulse

Arnulfo Silva / TI-ML-Audio-Classification Public

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

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

Did you know? You can bring your own DSP code.

Description	Author	Recommended
Audio (MFCC) Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	Edge Impulse
Audio (MFE) Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.	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.	Syntiant
Raw Data Use data without pre-processing. Useful if you want to use deep learning to learn features.	Edge Impulse
Audio (MFCC) Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	Edge Impulse
Audio (MFE) Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.	Edge Impulse
Flatten Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.	Edge Impulse
Image Preprocess and normalize image data, and optionally reduce the color depth.	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.	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

Did you know? You can bring your own model in PyTorch, Keras or scikit-learn.

Description	Author	Recommended
Classification Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	Edge Impulse
Regression Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	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.	Edge Impulse
Anomaly Detection (GMM) Enterprise Find outliers in new data. A Gaussian mixture model (GMM) models the shape of data using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	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
FOMO-AD (Images) Enterprise Visual anomaly detection. Find outliers in new data. Extracts visual features using a pre-trained model on your data, and a Gaussian mixture model (GMM) models the shape of the features using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	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.	Edge Impulse
Object Detection (Images) Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets.	Edge Impulse
Regression Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	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.	Edge Impulse
Anomaly Detection (GMM) Enterprise Find outliers in new data. A Gaussian mixture model (GMM) models the shape of data using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	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) Enterprise Visual anomaly detection. Find outliers in new data. Extracts visual features using a pre-trained model on your data, and a Gaussian mixture model (GMM) models the shape of the features using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	Edge Impulse

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Time series data

Input axes

audio

Window size

Window increase

Frequency (Hz)

Invalid frequency

Zero-pad data

Audio (MFCC)

Name

Input axes (1)

audio

Classification (Keras)

Name

Input features

MFCC

Output features

3 (Clockwise, Drive, Noise)

Output features

3 (Clockwise, Drive, Noise)

Time series data

Audio (MFCC)

Classification (Keras)

Output features

No data collected yet

Description