# Deep Learning-Based Sound Source Localization: A Review
###### Abstract  
As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. sound source localization; deep learning; model architectures; robustness; complex environment 2020 IEEE/RSJ Web Conference 2020 IEEE/provides indispensable capabilities across public security, marine exploration [1; 2], smart home interaction [3], and airspace surveillance [4]. In security systems, precise SSL enables rapid localization of emergency events. For marine monitoring, its accuracy in underwater target detection directly impacts national security and ecological studies. In smart homes, SSL-based voice interfaces must overcome complex reverberation to enhance user experience. However, traditional methods--including beamforming [5], time-delay estimation [6], and MUSIC algorithm [7]--rely on manually designed physical models and idealized assumptions, proving inadequate for dynamic real-world scenarios. Specific challenges include spatiotemporal heterogeneity of sound speed profiles in marine environments, high reverberation with a low signal-to-noise ratio (SNR) in indoor spaces, and non-stationary UAV noise characteristics [8; 9], which significantly degrade traditional localization accuracy or cause complete failure. Recent breakthroughs in deep learning have revolutionized SSL through three key advantages: end-to-end feature adaptability, data-driven modeling scalability, and cross-scenario generalization. In marine applications, complex-valued convolutional networks achieve centimeter-level precision under a low SNR via phase information fusion. For indoor reverberant environments, attention mechanisms combined with multimodal architectures suppress multi-source interference through adaptive acoustic feature weighting.