Neighbor-Consistent Neural Filters for Robust Personal Sound Zones Under Localization Uncertainty
Abstract
Coordinate-conditioned neural networks can generate head-tracked personal sound zone (PSZ) loudspeaker filters in real time, but they are sensitive to localization uncertainty. Small fluctuations in estimated listener coordinates, caused by optical distortion, temporary occlusions, or tracking jitter, may produce large filter changes even when listeners are physically stationary. This paper proposes neighbor-consistent neural filters that regularize the coordinate-to-filter mapping by penalizing filter differences at randomly perturbed neighboring coordinates during training. To evaluate robustness against tracking noise, we introduce a decoupled protocol that fixes the acoustic transfer functions at a physical anchor while perturbing only the coordinate inputs used for filter generation. Isolation quality and local stability are evaluated using neighborhood median and lower-tail statistics of inter-zone and inter-program isolation, together with spatial variation rates that quantify metric sensitivity within a coordinate neighborhood. In simulation with a split-band woofer-tweeter system and 25 randomly sampled anchor positions, neighbor consistency reduces the root-mean-square (RMS) variation rate by up to 55.9% in the woofer band and 30.3% in the tweeter band while largely preserving isolation quality and improving lower-tail robustness. In in-situ measurements using a 24-driver array and two stationary head-and-torso simulators, the proposed regularization improves worst-case neighborhood isolation by up to 16.9% and reduces spatial variation rates by up to 61.8%. These results demonstrate that neighbor-consistency regularization effectively stabilizes PSZ rendering under localization uncertainty.
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 main contribution of this paper is the introduction of neighbor-consistent neural filters that effectively stabilize personal sound zone rendering under localization uncertainty. This work represents a meaningful advancement in audio processing, addressing a critical challenge in the field and providing a foundation for future research on robust audio systems.
Methodology
The paper introduces a novel approach to enhance the robustness of personal sound zones (PSZs) against localization uncertainty through neighbor-consistent neural filters. The methodology employs a regularization technique that penalizes filter differences at perturbed neighboring coordinates, effectively stabilizing the coordinate-to-filter mapping. This innovative approach is well-grounded in existing literature, yet it distinguishes itself by addressing the specific issue of localization uncertainty in PSZ systems, which has not been sufficiently tackled in prior works. The proposed decoupled evaluation protocol is also a significant methodological advancement, allowing for a clearer assessment of robustness independent of physical listener motion.
Results & evaluation
The experiments conducted in both simulation and in-situ measurements provide a comprehensive evaluation of the proposed method. The use of a split-band woofer-tweeter system and the detailed metrics for isolation quality and spatial stability demonstrate a rigorous approach to validating the effectiveness of the neighbor-consistency regularization. The results indicate substantial improvements in stability metrics while preserving isolation quality, showcasing the practical applicability of the proposed method in real-world scenarios.
Reproducibility
The paper lacks specific implementation details that would facilitate reproducibility, such as code availability or detailed hyperparameter settings beyond the sensitivity analysis. While the methodology is well described, the absence of a public repository or demo limits the ability of other researchers to replicate the findings independently.
Limitations
One limitation of the study is the focus on a specific configuration of the loudspeaker system, which may not generalize to all PSZ applications. Additionally, while the proposed method shows promise in improving robustness, the long-term effects of neighbor-consistency regularization on overall audio quality and listener experience remain unexplored. The paper also does not address potential computational overhead introduced by the additional regularization during training.
Broader impact
The findings of this research have significant implications for the development of robust audio systems in shared environments, such as vehicles and public spaces, where accurate sound localization is critical. The proposed method could enhance user experience by providing stable and high-fidelity audio zones, thereby facilitating applications in personal audio systems and immersive sound environments. The main contribution of this paper is the introduction of neighbor-consistent neural filters that effectively stabilize personal sound zone rendering under localization uncertainty. This work represents a meaningful advancement in audio processing, addressing a critical challenge in the field and providing a foundation for future research on robust audio systems.
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