# Deep Learning-Based Sound Source Localization: A Review
## 5 Conclusions  
The rapid advancement of deep learning has catalyzed a paradigm shift in acoustic source localization, offering transformative solutions through end-to-end adaptive feature learning, data-driven modeling, and cross-domain generalization. In marine environments, convolutional neural networks (CNNs) and U-Net architectures address multipath interference and environmental mismatch by adaptively extracting spectral-temporal features from acoustic signals. Complex-valued CNNs further enhance precision in low-SNR deep-sea conditions by explicitly encoding acoustic phase information, achieving centimeter-level localization accuracy. While adversarial domain adaptation techniques improve robustness to dynamic sound speed profiles, challenges persist in acquiring large-scale annotated datasets and ensuring real-time inference in fluctuating underwater environments. Future advancements may integrate physics-informed neural architectures that embed differen \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Tech** & **Use** & **Pros** & **Cons** \\ \hline CNN & Spectral conv & Indoor/UAV & Efficient local features & Multi-source overlap \\ U-Net & Encoder–decoder & Marine/Indoor & Multi-task output & Heavy computation \\ GFGRU & GRU + feature fusion & UAV noise/Indoor & Dynamic noise filtering & Data-dependent, unstable \\ CRNN & CNN + RNN & UAV audio & Spectral–temporal fusion & Slow inference \\ Transformer & Self-attention & Low SNR & Global context & High resource demand \\ Lightweight CNN & Depthwise conv & Edge devices & Real-time, low power & Accuracy trade-off \\ Complex CNN & Complex conv & Underwater & Low-SNR robustness & Hardware-intensive \\ GAN & Adversarial nets & Data scarcity & Reduces labeling & Training instability \\ ResCNN & Residual CNN & Marine fields & Deep modeling & High computational needs \\ LSTM & Long-term memory & UAV tracking & Temporal patterns & Sensitive to noise bursts \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of strengths and limitations. tiable wave propagation equations, coupled with hybrid frameworks combining GAN and simulation-to-reality data co-optimization to mitigate domain gaps. Indoor localization systems face unique challenges in high-reverberation and multi-source overlapping scenarios. Attention mechanisms dynamically prioritize critical acoustic features to suppress reverberation artifacts, while lightweight CNN with depthwise separable convolutions enable edge deployment on smart devices. However, performance degradation in complex multi-source environments remains unresolved. Emerging solutions explore acoustic-visual-infrared multimodal fusion to exploit complementary sensory data, alongside spatiotemporal Transformer networks that model long-range dependencies for robust source separation. Hardware-accelerated architectures leveraging FPGA or ASIC platforms show promise for achieving millisecond-level inference speeds, essential for real-time applications in dynamic indoor settings. Drone-based acoustic monitoring benefits from spatiotemporal Transformers and GNN, which capture non-stationary noise characteristics through spectral-temporal analysis. Distributed micro-microphone arrays paired with lightweight models facilitate low-power edge deployment, yet background noise interference and sensor layout generalization limit practical efficacy. Meta-learning frameworks aim to bridge simulation-to-reality knowledge transfer, while differential privacy techniques anonymize sensitive frequency bands to address ethical concerns in surveillance applications.