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Hungarian Network (Hnet)

The Hungarian Network (Hnet) is the deep-learning-based implementation of the popular Hungarian algorithm that helps solve the assignment problem. Implementing this algorithm using a DNN allows us to integrate it with other deep-learning tasks that require permutation invariant training (PIT) and train them in a completely differentiable manner without the need of PIT. Some examples of deep-learning tasks that required PIT are multi-source localization (DOA estimation), and source separation. One such implementation of multi-source localization and tracking using Hnet is available here. If you want to read more about generic approaches to sound localizatoin and tracking then check here. If you are using this repo in any format, then please consider citing the following paper.

Sharath Adavanne*, Archontis Politis* and Tuomas Virtanen, "Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers" in the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2021)

METHOD

The Hnet architecture is as shown below. The input is the pair-wise distance matrix D, that is mapped to the association matrix A.

The output A of Hnet is in the continuous range of [0 1], where indices with value 1 suggests the true associations.

Getting Started

This repository consists of three Python scripts

  • The generate_hnet_training_data.py is a standalone script that generates data to train the Hnet for multi-source localization as described in the cited paper above.
  • The train_hnet.py script consists of all the training, model, and feature parameters.
  • visualize_hnet_results.py script to visualize the Hnet output

Prerequisites

The provided codebase has been tested on Python 3.8.1 and Torch 1.10

Training the Hnet

In order to quickly train Hnet follow the steps below.

  • First, create the training data by running generate_hnet_training_data.py. It generates pairs of distance matrix D and corresponding associate matrix A. These distance matrices are currently set to be angular distance between two polar coordinates on a unit-sphere. You can modify this code to the range of distances corresponding to your task. Read the comments to understand more details.
python3 generate_hnet_training_data.py
  • You can now train the Hnet using default parameters. It can train on quickly even on a laptop CPU. GPU is not mandatory.
python3 train_hnet.py
  • You can visualize the output of Hnet with the following command.
python3 visualize_hnet_results.py

License

The repository is licensed under the TAU License.