Glass breaking

Task: Audio Classification

License: Apache 2.0

Dataset Screenshot

Description

This dataset has been generated by Edge Impulse teams to recognize the sound of glass breaking.

The synthetic data has been generated using the Edge Impulse's ElevenLabs.io integration.

You can also have a look at the blog post That Sounds Great: Create Ultra-Realistic Audio Datasets with ElevenLabs.io.

Compatible Blocks

• Feature extraction: Audio (MFE), Spectrogram, Raw Data

• Learning block: Classification

Not sure what to choose? Try out this dataset with the EON Tuner.

Dataset Details

• Total Data Items: 500

• Total Data Length: 0h 21m 15s

• Axis Summary: audio

• Labeling Method: single label

• Train/Test Split: 80.00% / 20.00%

	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

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.

• Download

  • Direct link

  • 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:

  • Studio uploader

  • CLI uploader

  • CSV Wizard

  • Python SDK

  • Ingestion API

  • Import from S3 buckets

  • Upload portals (Enterprise feature)

Citation

If you use this dataset in your research paper, please cite it using the following BibTeX:

@misc{edgeimpulse_dataset_497425,
    title = {Audio Classification - Glass breaking},
    author = {Edge Impulse},
    year = {2024},
    url = {https://studio.edgeimpulse.com/public/497425/latest},
    note = {Apache 2.0}
}