Neural-SRP

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.

Configuration

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.

Main scripts

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.

Pretrained models

You can find the pretrained models in the checkpoints folder.

Datasets

• 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.