Task: Audio Classification
License: Apache 2.0
Have you ever wanted to make your own "Ok, Google" or "Alexa" keyword spotting model? The helloworld class has been collected by Edge Impulse teams, the added noise samples come from the Microsoft Scalable Noisy Speech Dataset and the unknown samples are based on a subset of data in the Google Speech Commands Dataset.
This dataset can be used to build an Edge AI project detecting the "Hello World" keyword phrase.
You can also follow our tutorial to guide you through building your keyword spotting model, from data collection to deployment on embedded devices.
Feature extraction: Audio (MFCC), Audio (MFE), Spectrogram, Raw Data
Learning block: Classification, Transfer Learning (Keyword Spotting)
Not sure what to choose? Try out this dataset with the EON Tuner.
Total Data Items: 2062
Total Data Length: 0h 34m 22s
Axis Summary: audio
Labeling Method: single label
Train/Test Split: 79.97% / 20.03%
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.
Download
HuggingFace - Soon
Kaggle - Soon
Import this dataset to your Edge Impulse project
This project uses the Edge Impulse Exporter Format (info.labels). See this documentation page for more info.
Edge Impulse also supports different data sample formats and dataset annotation formats that you can import into your project to build your edge AI models:
Upload portals (Enterprise feature)
If you use this dataset in your research paper, please cite it using the following BibTeX:
Task: Audio Classification
License:
This dataset has been generated by Edge Impulse teams to recognize the sound of glass breaking.
Total Data Items: 500
Total Data Length: 0h 21m 15s
Axis Summary: audio
Labeling Method: single label
Train/Test Split: 80.00% / 20.00%
Download
HuggingFace - Soon
Kaggle - Soon
Import this dataset to your Edge Impulse project
If you use this dataset in your research paper, please cite it using the following BibTeX:
Task: Audio Classification
License:
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.
Total Data Items: 18
Total Data Length: 0h 15m 40s
Axis Summary: audio
Labeling Method: single label
Train/Test Split: 88.89% / 11.11%
Download
HuggingFace - Soon
Kaggle - Soon
Import this dataset to your Edge Impulse project
If you use this dataset in your research paper, please cite it using the following BibTeX:
The synthetic data has been generated using the .
You can also have a look at the blog post .
Feature extraction: , ,
Learning block:
Not sure what to choose? Try out this dataset with the .
Clone the .
To clone and use this project, visit the , click on the Clone button on the top-right corner and follow the cloning instructions.
This project uses the Edge Impulse Exporter Format (info.labels). See this for more info.
Edge Impulse also supports different and that you can import into your project to build your edge AI models:
(Enterprise feature)
You can also follow 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.
Feature extraction: , ,
Learning block:
Not sure what to choose? Try out this dataset with the .
Clone the .
To clone and use this project, visit the , click on the Clone button on the top-right corner and follow the cloning instructions.
This project uses the Edge Impulse Exporter Format (info.labels). See this for more info.
Edge Impulse also supports different and that you can import into your project to build your edge AI models:
(Enterprise feature)
Training Set
Testing Set
Total Data Items
1649
413
Labels
helloworld, noise, unknown
helloworld, noise, unknown
Total Data Length
0h 27m 29s
0h 6m 53s
Training Set | Testing Set |
Total Data Items | 400 | 100 |
Labels | glass_breaking, noise | glass_breaking, noise |
Total Data Length | 0h 17m 37s | 0h 3m 37s |
Training Set | Testing Set |
Total Data Items | 16 | 2 |
Labels | faucet, noise | faucet, noise |
Total Data Length | 0h 13m 40s | 0h 2m 0s |
