DeepWhaleNet: An FFT-based Deep Neural Network for Passive Acoustic Monitoring

Author: Nicholas Ryan Rasmussen - University of South Dakota - Computer Science Department - 2AI Lab

This code was designed for the IWC-SORP/SOOS Acoustic Trends Annotated Library downloaded as of 1-30-23

Installation

Follow these steps to install and run the project:

1. Download dataset from The IWC-SORP/SOOS Acoustic Trends Annotated Library:

http://data.aad.gov.au/metadata/records/AcousticTrends_BlueFinLibrary

2. Clone the repository:

git clone https://github.com/2ai-lab/DeepWhaleNet.git

3. Navigate to the project directory:

cd DeepWhaleNet

4. Move Annotated Library into new Repository:

cp -R /path/to/AcousticTrends_BlueFinLibrary/* /path/to/DeepWhaleNet/ ## Linux

xcopy /E /I C:\path\to\AcousticTrends_BlueFinLibrary\* C:\path\to\DeepWhaleNet\  ## Windows

5. Set up a virtual environment (optional but recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

6. Install the required packages:

pip install -r requirements.txt

7. Run the project:

python runAll.py  # Replace with your main script