Quantitative Measures for Passive Sonar Texture Analysis:

Quantitative Measures for Passive Sonar Texture Analysis

Jarin Ritu, Alexandra Van Dine and Joshua Peeples

Note: If this code is used, cite it: Jarin Ritu, Alexandra Van Dine and Joshua Peeples. (2025, April 22) Peeples-Lab/Passive_Sonar_Texture_Analysis : Initial Release (Version v1.0). Zendo.https://zenodo.org/records/15264875

10.5281/zenodo.15264875

arXiv

BibTeX

In this repository, we provide the paper and code for "Quantitative Measures for Passive Sonar Texture Analysis."

Installation Prerequisites

The requirements.txt file includes all 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.py in Python IDE (e.g., Spyder) or command line to train, validate, and test models. To evaluate performance, run View_Results.py (if results are saved out).

Main Functions

The target classification code uses the following functions.

1. Intialize model

   model, input_size, feature_layer = intialize_model(**Parameters)

2. Prepare dataset(s) for model

   dataloaders_dict = Prepare_Dataloaders(**Parameters)

3. Train model

   train_dict = train_model(**Parameters)

4. Test model

   test_dict = test_model(**Parameters)

Parameters

The parameters can be set in the following script:

Demo_Parameters.py

Inventory

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

└── root directory
    ├── demo.py                                   // Run this. Main demo file.
    ├── Demo_Parameters.py                        // Parameter file for the demo.
    ├── Prepare_Data.py                           // Load dataset for the demo. 
    ├── View_Results.py                           // Run this after the demo to view saved results. 
    └── Datasets                
        ├── Get_Preprocessed_Data.py              // Resample the audio data and generate segments for the dataset.
        └── DeepShipSegments.py                   // Create a custom dataset for DeepShip/VTUAD/Synthetic
    └── Utils                     
        ├── Class_information.py                  // Class names for dataset for the results script.
        ├── Compute_FDR.py                        // Compute Fisher’s discriminant ratio.
        ├── Confusion_mats.py                     // Create and plot confusion matrix.
        ├── Generate_Learning_Curves.py           // Generate learning curves for training and validation.
        ├── Generate_TSNE_visual.py               // Generate TSNE visuals.
        ├── Generate_Spatial_Dims.py              // Compute spatial dimensions for histogram layer models.
        ├── Get_Optimizer.py                      // Define a set of optimizers to choose from.
        ├── Histogram_Model.py                    // Load histogram model with TDNN (or any backbone network).
        ├── Network_functions.py                  // Contains functions to initialize, train, and test the model.
        ├── RBFHistogramPooling.py                // Create histogram layer.
        ├── Save_Results.py                       // Save results from the demo script.
        ├── pytorchtools.py                       // Implement early stopping to terminate training based on validation metrics.
        └── Feature_Extraction_Layer.py           // Extract and transform different features from the audio files.
        └── TDNN.py                               // Baseline TDNN model.
        └── synthetic_statistical_texture.py      // Create synthetic statistical texture dataset.
        └── synthetic_structural_texture.py       // Create synthetic structural texture dataset.
        └── synthetic_mixed_texture.py            // Create synthetic mixed statistical and structural textures dataset.
        └── StaTS.py                              // Quantify statistical texture.
        └── StrTS.py                              // Quantify structural texture.

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) 2025 J. Ritu, A. Dine and J. Peeples. All rights reserved.

Citing Quantitative Measures for Passive Sonar Texture Analysis

If you use the Quantitative Measures for Passive Sonar Texture Analysis code, please cite the following reference using the following entry.

BibTex:

@article{ritu2025quantitative,
  title={Quantitative Measures for Passive Sonar Texture Analysis},
  author={Ritu, Jarin and Van Dine, Alexandra and Peeples, Joshua},
  journal={arXiv preprint arXiv:2504.14843},
  year={2025}
}