Tutorials written in Jupyter Notebooks to guide you through both accessing and using the data in the AWS pacific-sound registry.
Tutorials
- 2kHz Data ✨ Recommended first step to learn more about the low-frequency data
- 16kHz Data ✨ Recommended first step to learn more about the low-mid-frequency data
- 256kHz Data ✨ Recommended first step to learn more about the raw 10-minute data
- 256kHz to 2kHz Decimation ✨ Recommended to learn about how the raw 10-minute data is decimated from 256kHz to 2Khz
- Blue Whales
- Blue B call index 🐳 Study song occurrence using a signal processing method
- Blue A call classification 🐳 Classify blue whale song A calls with a neural network model
- Fin Whales
- Fin whale call index 🐳 Fin whale song occurrence using a signal processing method
- Humpback Whales
- Humpback whale song detection 🐳 Detect humpback song with a neural network model
- Shipping Noise
- Quantify shipping noise in the soundscape 🛳️ Apply international standards to measure shipping noise and its temporal variations.
- Full access to 16 kHz audio 🐬 Listen to example recordings of dolphins and whales, then pick any time to listen and explore.
See pacific-sound for the official documentation.
Python>=3.6.0 is required with the requirements.txt.
The recommended way to run the notebooks is using the Anaconda environment
git clone https://github.com/mbari-org/pacific-sound-notebooks
cd pacific-sound-notebooksIf using a Mac
brew install sox
If using Linux
apt-get install libsox-fmt-all libsox-dev
conda env create
conda activate pacific-sound-notebooks
pip install ipykernel
python -m ipykernel install --user --name=pacific-sound-notebooks
jupyter notebook