LEAVES is a powerful ecoacoustics tool designed to streamline the annotation and visualization of large-scale natural soundscape datasets. By leveraging advanced machine learning, real-time analysis, and a user-friendly interface, LEAVES empowers researchers and citizen scientists to process and label their data efficiently.
- Efficient Labeling: Reduces annotation time for large datasets with cluster-based workflows.
- Interactive Visualizations: Explore your data in 3D scatterplots, spectrograms, and waveforms.
- Customizable Configuration: Flexible settings for preprocessing, clustering, and visualization.
- Multi-Format Support: Works with
.WAV,.MP3,.FLAC, and more. - Real-Time Spectrograms: Analyze acoustic features while annotating.
- Cluster Filtering: Focus on specific sound groups for detailed examination.
Get the most out of LEAVES with the LEAVES User Guide, which includes:
- Step-by-step setup instructions.
- Detailed explanations of features like File Uploading, Annotation, Cluster Filtering, and more.
- Visual examples and troubleshooting tips.
Follow these steps to install and start using LEAVES:
git clone https://github.com/thomasnapier/LEAVES.gitNavigate to the project directory and install the required Python libraries:
pip install -r requirements.txtStart the web-based interface by running:
python app.pyOpen your browser and navigate to http://127.0.0.1:8050.
Use the Upload Module to import your audio recordings and visualize them in 3D scatterplots.
Process, annotate, and save your data using LEAVES' advanced clustering and annotation tools.
- Data Ingestion: Upload audio recordings in
.WAV,.MP3,.FLAC, or other supported formats. - Signal Processing: Denoising, short-term windowing, and feature extraction (e.g., MFCCs) prepare your data for analysis.
- Dimensionality Reduction: Techniques like UMAP and t-SNE reduce data complexity for intuitive visualization.
- Clustering: Algorithms like DBSCAN and k-means group similar sounds for efficient labeling.
- Annotation: Assign labels to clusters or individual points, with propagation features to save time.
- Visualization: Interactive tools like waveforms, spectrograms, and 3D scatterplots aid exploration and analysis.
- Technologies Used: Python 3, Dash, Plotly, Librosa, Scikit-Learn.
- Current Status: Alpha Version.
- Demo Instance: Try the Demo
- Documentation: User Guide
- Project Site: Comprehensive guide and instructions for LEAVES features.
- Installation Guide: Detailed instructions for setup.
- Demo Instance: Explore a hosted version of LEAVES.
LEAVES is licensed under the MIT License. See LICENSE for details.
For questions or feedback, feel free to reach out:
- Author: Thomas Napier
- GitHub: Thomas Napier