Histogram Layer Time Delay Neural Networks For Passive Sonar Classification
Jarin Ritu, Ethan Barnes, Riley Martell, Alexandra Van Dine and Joshua Peeples
Note: If this code is used, cite it: Jarin Ritu, Ethan Barnes, Riley Martell, Alexandra Van Dine and Joshua Peeples. (2023, July 25) Peeples-Lab/HLTDNN: Initial Release (Version v1.0).
Zendo.https://zenodo.org/record/8184517
In this repository, we provide the paper and code for "Histogram Layer Time Delay Neural Network For Passive Sonar Classification."
The requirements.txt file includes all the necessary packages, and the packages will be installed using:
pip install -r requirements.txt
Or, for a more convenient option, a pre-configured environment is available for download through the provided link. This environment comes with all the essential packages already installed, ensuring a hassle-free setup process.
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).
[The results may vary from the paper as we updated the data loading and data normalization]
The target classification code uses the following functions.
-
Intialize model
model, input_size, feature_layer = intialize_model(**Parameters) -
Prepare dataset(s) for model
dataloaders_dict = Prepare_Dataloaders(**Parameters) -
Train model
train_dict = train_model(**Parameters) -
Test model
test_dict = test_model(**Parameters)
The parameters can be set in the following script:
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.
└── 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.
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) 2023 J. Ritu, E. Barnes, R. Martell, A. Dine and J. Peeples. All rights reserved.
If you use the Passive Sonar Classification classification code, please cite the following reference using the following entry.
Plain Text:
J. Ritu, E. Barnes, R. Martell, A. Dine and J. Peeples, "Histogram Layer Time Delay Neural Network For Passive Sonar Classification," 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1-5, doi: 10.1109/WASPAA58266.2023.10248102.
BibTex:
@inproceedings{Ritu2023histogram,
title={Histogram layer time delay neural network for passive sonar classification},
author={Ritu, Jarin and Barnes, Ethan, and Martell, Riley, and Van Dine, Alexandra, and Peeples, Joshua},
booktitle={2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
pages={TBD},
year={2023},
organization={IEEE}
}