# Deep Learning-Based Sound Source Localization: A Review
## 4 Strengths and Limitations of Deep Learning Methods  
CNNs have demonstrated remarkable efficacy in indoor single-source localization and drone-based acoustic detection due to their localized feature extraction capabilities and computational efficiency. For instance, multi-scale convolutional kernels enable high-precision azimuth estimation in low-reverberation environments by capturing spectral features. However, CNNs exhibit limitations in modeling multi-source overlapping signals and long-term temporal dependencies, rendering them less adaptable to dynamic noise scenarios. In contrast, U-Net's encoder-decoder architecture supports multi-task outputs, such as joint estimation of distance and depth in marine environments. Despite its ability to process complex acoustic field data, U-Net's substantial parameter count and computational overhead restrict real-time performance, making it more suitable for offline applications. To address non-stationary interference in drone noise and indoor reverberant environments, GFGRU leverage temporal feature fusion through gating mechanisms and cross-layer feedback. Yet, their reliance on high-quality annotated data and convergence instability hinder practical deployment efficiency.  
Lightweight CNNs, employing depthwise separable convolutions and parameter compression techniques, enable low-power real-time monitoring on edge devices like oceanic buoys and drones. However, these models suffer from significant accuracy degradation, particularly under low signal-to-noise ratio (SNR) conditions. Conversely, complex-valued  
Figure 9: (Color online) Framework of the proposed method [26]. Light blue arrow: Fix pre-trained generator. White arrow: Source data forward pass. Orange arrow: Features to discriminator via GRL.  
convolutional networks achieve a 20-30% reduction in localization errors in deep-sea low-SNR environments by integrating acoustic phase information through complex-domain operations. Nevertheless, their heightened hardware requirements and model complexity necessitate high-performance computing platforms, limiting their edge deployment. Transformers, while exhibiting strong robustness in complex noise environments via global self-attention mechanisms, face challenges in small-scale labeled scenarios due to prohibitive computational complexity and data demands.  
Current methodologies are constrained by three core limitations: Inadequate integration of physical principles: Traditional CNNs lack explicit modeling of acoustic wave propagation equations, restricting generalization in dynamic marine environments. Data dependency trade-offs: Although GANs alleviate annotation scarcity through domain adaptation, their performance is hindered by adversarial training instability and synthetic data quality issues. Real-time-precision imbalance: Architectures like residual CNNs, despite enabling deep acoustic field modeling, struggle with edge deployment due to excessive parameter counts and computational loads.  
Future research should prioritize physics-informed architectures that embed domain-specific knowledge (e.g., differentiable wave equation solvers), unsupervised domain adaptation techniques to bridge simulation-to-reality gaps, and hardware-algorithm co-design for resource-constrained platforms. For instance, integrating neural architecture search with model compression could optimize edge-device efficiency, while hybrid approaches combining CNNs with spectral graph theory may enhance multi-source separation. These advancements will drive acoustic localization technologies toward intelligent systems characterized by high precision, robustness, and energy efficiency, enabling transformative applications in underwater exploration, autonomous drones, and adaptive smart environments (see Table 4).