Diffusion-Based Sound Source Localization Using a Distributed Network of Microphone Arrays

Area of Science:

• Acoustics and Signal Processing
• Distributed Systems
• Optimization Theory

Background:

• Traditional microphone array networks for 3D sound source localization (SSL) often depend on centralized data processing, posing challenges to scalability and robustness.
• Existing methods may struggle with network connectivity and varying acoustic conditions like reverberation and signal-to-noise ratio (SNR).

Purpose of the Study:

• To reformulate 3D sound source localization (SSL) as a distributed optimization problem for enhanced network performance.
• To introduce and evaluate a computationally distributed Adapt-Then-Combine (ATC) strategy for microphone array networks.
• To develop adaptive cooperation strategies that improve localization accuracy by penalizing underperforming arrays.

Main Methods:

• Recasting 3D SSL with microphone array networks as a distributed optimization problem.
• Implementing a computationally distributed Adapt-Then-Combine (ATC) diffusion strategy.
• Developing adaptive cooperation strategies using error-based and distance-based penalties for array performance.
• Evaluating performance in simulated acoustic environments with varying reverberation and SNR levels.

Main Results:

• The proposed distributed ATC approach achieves high localization accuracy and stability.
• Adaptive cooperation strategies effectively penalize detrimental array contributions, improving overall performance.
• The method demonstrates robustness even in sparsely connected networks.

Conclusions:

• A distributed optimization framework offers a scalable and robust solution for 3D sound source localization.
• Adaptive cooperation strategies are crucial for optimizing performance in decentralized microphone array systems.
• The presented approach maintains accuracy and stability across diverse acoustic conditions and network topologies.