Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models:

Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models

Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra Van Dine and Joshua Peeples

Note: If this code is used, cite it: Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra Van Dine and Joshua Peeples. (2024, August 30) Peeples-Lab/PANN_Models_DeepShip: Initial Release (Version v1.0). Zendo.https://zenodo.org/records/13886743

In this repository, we provide the paper and code for "Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models."

Installation Prerequisites

The requirements.txt file includes the necessary packages, and the packages will be installed using:

pip install -r requirements.txt

Demo

To get started, please follow the instructions in the Datasets folder to download the dataset. Next, run demo_light.py in Python IDE (e.g., Spyder)

Parameters

The parameters can be set in the following script:

Demo_Parameters.py

Inventory

https://github.com/Peeples-Lab/PANN_Models_DeepShip

└── root directory
    ├── demo_light.py                     // Run this. Main demo file.
    ├── Demo_Parameters.py                // Parameter file for the demo.
    └── Datasets                
        ├── Get_Preprocessed_Data.py      // Resample the audio data and generate segments for the dataset.
        ├── SSDataModule.py               // Load and preprocess the dataset.
    └── Utils                     
        ├── Network_functions.py          // Contains functions to initialize the modelS.
        ├── PANN_models.py          	  // Contains the PANN modelS.
        ├── LitModel.py                   // Prepare the PyTorch Lightning framework.

License

This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.

This product is Copyright (c) 2024 A. Mohammadi, T. Kelhe, D. Carreiro, A. Dine and J. Peeples. All rights reserved.