Topology-independent distributed multichannel Wiener filter (TI-dMWF)

This repository contains the complete parametrizable simulation framework to:

1. Simulate topology-unconstrained wireless acoustic sensor networks (WASNs) in a reverberant acoustic environment.
2. Perform noise reduction (signal estimation) using basic multichannel Wiener filters - either in a centralized fashion at a hypothetical central processing unit, or at the node level.
3. Perform distributed noise reduction (distributed signal estimation) using the dMWF [1], DANSE [2], or rS-DANSE [3] for fully connected WASNs, or TI-dMWF [manuscript submitted to IEEE Open Journal of Signal Processing], TI-DANSE [4], or TI-DANSE+ [5] for non-fully connected WASNs.
4. Post-process simulations results, compute and visualize signal enhancement / noise reduction objective metrics.

It is related to a Short Paper submitted to the IEEE Open Journal of Signal Processing.

Project Structure

• README.md: This file.
• config/: Configuration files (YAML).
• tools/: Core classes and utility functions.
• main.py: Entry point for running simulations. Requires PATH_TO_CFG global variable pointing to config file. Runs pp.py automatically after the simulation unless specified.
• pp.py: Entry point for solely post-processing simulations.
• precomb_pp.py: Script to combine outputs of several simulations before post-processing.

Getting Started

Prerequisites

• Python 3.8 or higher
• Dependencies listed in requirements.txt

Installation

1. Clone the repository: sh git clone https://github.com/p-didier/ti-dmwf_complete.git
2. Navigate to the project directory: sh cd ti-dmwf_complete
3. Install required packages (recommended in a virtual environment): sh pip install -r requirements.txt

Usage

1. Configure simulation parameters in the appropriate YAML file under config/. Remember to modify the file paths for the sound file databases.
2. Set the PATH_TO_CFG variable to your config file path.
3. If you want to run batch experiments, adjust testParams in main.py.
4. Run the main script: sh python main.py

Figure 1: Snippet from manuscript submitted to IEEE Open Journal of Signal Processing in March 2026. The misalignment metric is the mean squared error between estimated and true desired signal. The simulation is conducted in batch mode.

References

[1] P. Didier, T. van Waterschoot, S. Doclo, J. Bitzer, P. Behmandpoor, H. Gode, and M. Moonen, “Distributed Multichannel Wiener Filtering for Wireless Acoustic Sensor Networks,” 2025, submitted for publication in IEEE/ACM Trans. Audio, Speech, Language Process.

[2] A. Bertrand and M. Moonen, “Distributed Adaptive Node-Specific Signal Estimation in Fully Connected Sensor Networks—Part I: Sequential Node Updating,” IEEE Trans. Signal Process., vol. 58, no. 10, pp. 5277–5291, 2010.

[3] A. Bertrand and M. Moonen, “Distributed adaptive node-specific signal estimation in fully connected sensor networks—Part II: Simultaneous and asynchronous node updating,” IEEE Trans. Signal Process., vol. 58, no. 10, pp. 5292–5306, 2010.

[4] J. Szurley, A. Bertrand, and M. Moonen, “Topology-Independent Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks,” IEEE Trans. Signal Inf. Process. Netw., vol. 3, no. 1, pp. 130–144, 2017.

[5] P. Didier, T. van Waterschoot, S. Doclo, J. Bitzer, and M. Moonen, “Fast-Converging Distributed Signal Estimation in Topology-Unconstrained Wireless Acoustic Sensor Networks,” 2025, accepted for publication in IEEE Trans. Signal Inf. Process. Netw.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Paul Didier, KU Leuven university Belgium, STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Electrical Engineering Dept. (ESAT). PhD promotor: Prof. Marc Moonen.

For questions or issues, contact: pauldidier26@gmail.com.