Dataset Screenshot
Description
This dataset has been collected by Edge Impulse team to demonstrate how to perform Sensor Fusion Classification leveraging Neural Networks Embeddings. Sensor fusion refers to the process of combining data from different types of sensors to give more information to the neural network. To extract meaningful information from this data, you can use the same DSP block, multiples DSP blocks, or use neural networks embeddings. You can have a look at this tutorial for more information to see how to use this dataset. This dataset combines audio data and accelerometer data.Recommended Impulse
- Feature extraction: Audio Embeddings (Spectrogram + Convolution Neural Network Embeddings) + Spectral Analysis (accelerometer)
- Learning block: Classification using a fully-connected network
Dataset Details
- Total Data Items: 36
- Labeling Method: single label
- Train/Test Split: 91.67% / 8.33%
| Training Set | Testing Set | |
|---|---|---|
| Total Data Items | 33 | 3 |
| Labels | extract, grind, idle, pump |
Usage
- Clone the public project To clone and use this project, visit the Edge Impulse Studio link, click on the Clone button on the top-right corner and follow the cloning instructions.
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Download
- Direct link
- HuggingFace - Soon
- Kaggle - Soon
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Import this dataset to your Edge Impulse project
This project uses the Edge Impulse Exporter Format (
info.labels). See Edge Impulse labels for more info. Edge Impulse also supports different data acquisition formats and dataset annotation formats that you can import into your project to build your edge AI models: