# Deep Learning-Based Sound Source Localization: A Review
## 5 Conclusions
Despite the fact that current research on UAV acoustic localization has achieved sub-meter accuracy at a \(-10\) dB signal-to-noise ratio by means of lightweight CNN and spatio-temporal Transformer models, it still faces three core challenges: the mid-high frequency tonal components are susceptible to environmental noise interference, and the time-varying characteristics of noise spectra lead to insufficient model generalization; single-modal acoustic methods have difficulty in distinguishing co-frequency interference sources, and there is ambiguity in out-of-sight localization; the inference delay and computational power consumption of high-performance models on edge devices fail to meet the real-time tracking requirements. Multi-source data fusion represents a significant trend in UAVs. will comprehensively utilize multi-sensor data from satellite navigation, inertial navigation, vision, lidar, and acoustics. Through the mutual complementation and validation of multi-source data, the accuracy, stability, and anti-jamming capabilities of localization will be enhanced to adapt to more complex and diverse environments. For example, in areas with disturbed satellite signals such as indoor spaces or urban canyons, the combination of visual positioning and inertial navigation ensures stable UAV flight; in forest monitoring, fusing lidar data to construct 3D terrain models assists satellite navigation in achieving precise positioning. Underpinning these domain-specific advancements are three systemic challenges: the insufficient integration of wave physics in data-driven models, the reliance on costly annotated datasets, and unresolved trade-offs between algorithmic complexity and edge-device efficiency. To address these limitations, interdisciplinary efforts are converging on physics-guided architectures that couple neural operators with computational acoustic models, self-supervised learning paradigms utilizing contrastive learning and synthetic data augmentation, and hardware-algorithm co-design strategies incorporating NAS and adaptive model compression. The development of standardized datasets, open-source frameworks, and privacy-preserving protocols will be critical for fostering an ethical ecosystem to support scalable deployments in marine exploration, autonomous drones, and smart cities. By synergizing multimodal sensor fusion, computational physics, and energy-efficient edge computing, next-generation systems could achieve sub-centimeter precision with millisecond latency, ultimately advancing toward intelligent, sustainable, and socially responsible acoustic localization technologies. **Author Contributions:** Conceptualization, Z.Z. and K.X. ; investigation, K.X. ; resources, D.L. ; data curation, Z.Z. ; writing--original draft preparation, Z.Z. ; writing--review and editing, K.X. ; visualization, D.L. ; supervision, R.W. ; project administration, L.Y. ; funding acquisition, K.X., R.W. and L.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** China's National Natural Science Foundation Youth Fund (Project ID: 12002150); Ministry of Education of China's Youth Fund for Humanities and Social Sciences (Project ID: 24 YJAZH188); Open Project of the Key Laboratory of Aerodynamic Noise Control (ANCL20230302). **Acknowledgments:** The authors extend their sincere gratitude to the Key Laboratory of Aerodynamic Noise Control (ANCL20230302) for providing experimental support and technical guidance. We also appreciate the financial support from China's National Natural Science Foundation Youth Fund (Project ID: 12002150) and the Ministry of Education of China's Youth Fund for Humanities and Social Sciences (Project ID: 24 YJAZH188), which enabled the completion of this research. Additionally, we thank the reviewers for their constructive comments that significantly improved the clarity and depth of this review.