This repository contains the code for the Neural-SRP paper, published in the Open Journal of Signal Processing (OJSP). Neural-SRP is a neural network-based multi-source tracking algorithm which combines architectural features from SRP-PHAT, an established model-based algorithm for sound source localization.

This code contains the code used for training the Neural-SRP method under different scenarios, and also the code used for evaluating the performance of the trained models on the LOCATA and TAU-NIGENS datasets.

Parameters are controled in the params.json file. Note that some parameters should be changed depending on the script being run, as detailed below. The most important parameters are:

• model: the model to be trained. It can be neural_srp, doanet, cross3d or srp. doanet only works for the TAU-NIGENS (train_multisource.py) dataset.
• win_size: the window size in samples. Set to 640 when using the TAU-NIGENS dataset, and 4096 when using the LOCATA dataset.
• hop_size: the hop size in percentage. Set to 0.5 when using the TAU-NIGENS dataset, and 0.75 when using the LOCATA dataset.
• model_checkpoint_path: the path to the pretrained model. Must be compatible with the model parameter.

After setting the correct parameters in params.json, you can run the following scripts using python script_name.py. They are:

• visualize_locata.py Visualize single-source tracking trajectories on the LOCATA dataset. Generates results of Tables 3 and 4 in the paper.
• visualize_tau.py Visualize multi-source tracking trajectories on the TAU-NIGENS dataset. Generates results of Table 5 in the paper.
• train_singlesource.py trains the single-source models using simulated data.
• train_multisource.py trains the multi-source models using the TAU-NIGENS dataset.
• analyze_complexity.py analyzes the complexity of the different models. Generates results of Table 6 in the paper.

You can find the pretrained models in the checkpoints folder.

• LOCATA LOCATA challenge.
• TAU-NIGENS TAU-NIGENS dataset. After downloading, run the preprocessing script python -m datasets.preprocess_tau_nigens_dataset to generate the dataset in the correct format. Set the variable path_tau_nigens_preprocessed to the path of the output preprocessed dataset in params.json.