Dataset Screenshot
Description
This dataset has been collected by Edge Impulse teams to recognize the sound of water running from a faucet, even in the presence of other background noise. You can also follow our tutorial learn how to collect audio data from microphones, use signal processing to extract the most important information, and train a deep neural network that can tell you whether the sound of running water can be heard in a given clip of audio. Finally, you’ll deploy the system to an embedded device and evaluate how well it works.Compatible Blocks
- Feature extraction: Audio (MFE), Spectrogram, Raw Data
- Learning block: Classification
Dataset Details
- Total Data Items: 18
- Total Data Length: 0h 15m 40s
- Axis Summary: audio
- Labeling Method: single label
- Train/Test Split: 88.89% / 11.11%
| Training Set | Testing Set | |
|---|---|---|
| Total Data Items | 16 | 2 |
| Labels | faucet, noise | faucet, noise |
| Total Data Length | 0h 13m 40s | 0h 2m 0s |
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: