Physics-Guided Variational Model for Unsupervised Sound Source Tracking
Abstract
Sound source tracking is commonly performed using classical array-processing algorithms, while machine-learning approaches typically rely on precise source position labels that are expensive or impractical to obtain. This paper introduces a physics-guided variational model capable of fully unsupervised single-source sound source tracking. The method combines a variational encoder with a physics-based decoder that injects geometric constraints into the latent space through analytically derived pairwise time-delay likelihoods. Without requiring ground-truth labels, the model learns to estimate source directions directly from microphone array signals. Experiments on real-world data demonstrate that the proposed approach outperforms traditional baselines and achieves accuracy and computational complexity comparable to state-of-the-art supervised models. We further show that the method generalizes well to mismatched array geometries and exhibits strong robustness to corrupted microphone position metadata. Finally, we outline a natural extension of the approach to multi-source tracking and present the theoretical modifications required to support it.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel physics-guided variational model for unsupervised sound source tracking, effectively bridging machine learning and traditional signal processing techniques. The methodology is innovative, and the results demonstrate significant potential for real-world applications, marking a meaningful contribution to the field of audio processing.
Methodology
The proposed methodology integrates a variational autoencoder with a physics-based decoder, effectively combining machine learning with established physical principles to enhance sound source tracking. This innovative approach allows for unsupervised learning by leveraging geometric constraints without requiring labeled data, which is a significant advancement over traditional methods. The use of a von Mises-Fisher distribution for directional statistics is particularly noteworthy, as it is well-suited for the problem at hand. The architecture is designed for efficiency, allowing for parallel processing, which is crucial for real-time applications.
Results & evaluation
The experiments conducted are robust, comparing the proposed method against classical and state-of-the-art supervised models across various scenarios, including noise and uncertainty in microphone positioning. The results demonstrate that the proposed model not only outperforms classical methods but also competes well with supervised approaches, showcasing its effectiveness in real-world applications. The use of real-world data for testing adds credibility to the findings.
Reproducibility
The paper provides a detailed description of the methodology, including the architecture and loss functions, which facilitates reproducibility. However, the lack of a publicly available code repository or demo limits the ease with which other researchers can replicate the results.
Limitations
One limitation is the focus on single-source tracking, which may restrict the applicability of the method in more complex environments with multiple sound sources. Additionally, while the model performs well under various conditions, its performance relative to supervised models in all scenarios may not be consistent, particularly in highly reverberant environments.
Broader impact
This research has the potential to significantly impact fields such as robotics, hearing aids, and surveillance systems, where accurate sound source localization is critical. The unsupervised nature of the model could lead to more accessible implementations in devices that cannot afford extensive labeled training data. The paper presents a novel physics-guided variational model for unsupervised sound source tracking, effectively bridging machine learning and traditional signal processing techniques. The methodology is innovative, and the results demonstrate significant potential for real-world applications, marking a meaningful contribution to the field of audio processing.
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