# Deep Learning-Based Sound Source Localization: A Review
## 2 Classification of Sound Source Localization
### Indoor Sound Source Localization  
Traditional sound source localization methods, such as TODA [53; 54] and beamforming [55], rely on multi-channel microphone arrays and wave equation propagation models. TDOA estimates source coordinates via time delay differences from generalized cross-correlation, while beamforming forms spatial spectral peaks through covariance matrix optimization. These methods excel in ideal conditions but suffer from reduced accuracy in high-reverberation or low-SNR environments due to signal distortion and high computational complexity. Their reliance on precise array geometry and environmental models also limits adaptability in dynamic scenarios (see Table 2).  
Deep learning has revolutionized SSL by enabling data-driven feature learning. Architectures like deep neural networks [56], convolutional neural networks [28; 57], and recurrent neural networks [29] enable end-to-end acoustic feature extraction, outperforming traditional methods in noisy/reverberant conditions. For example, [PERSON] et al. integrated CNN with TDOA for regional localization [28], while [PERSON] et al. used DNN with room-specific reverberation modeling to enhance robustness [30]. [PERSON] et al. combined CNN with regression for phase difference-based SSL (see Figure 3) [31], and [PERSON] et al. improved accuracy via deep learning-assisted sound intensity estimation [32]. Recent breakthroughs focus on reverberation suppression and unsupervised domain adaptation,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Methods** & **Performance** & **Dataset** & **Scenes** \\ \hline
[13] & CNN & Seabed type prediction accurate, range error \(<\) 0.5 km (\(>\)5 km) & Simulated data + field data & Long-range deep-sea localization \\
[14] & U-Net + CBAM & Localization error \(\downarrow\) 32\% & Simulated data + South China Sea field data & Shallow-sea source localization in the South China Sea \\
[15] & GFGRU & Multi-source error near SBL, better than Bartlett/FNN & Simulated data + South China Sea field data & Deep-sea source localization \\
[16] & CNN + Transformer & Range error \(\downarrow\) 35\% vs. \(\mathrm{FNN}/\)pure CNN & Field data & Underwater source \\
[17] & APRCNN & 98.4\% accuracy at 0 dB SNR & Field data & target localization \\
[18] & CNN & Angular RMSE 0.5\({}^{\circ}\), \(\downarrow\) 58\% vs. traditional methods & Simulated data + field data & DOA estimation in marine environments \\
[19] & UDA + SOO & Error \(<\) 200 m at 20\({}^{\circ}\) array tilt & Simulated data + South China Sea field data & Deep-sea source localization \\
[20] & GAN & Transfer accuracy 92.7\%, \(\uparrow\) 18\% & Simulated data + field data & Horizontal line array source localization \\
[21] & MRP-CNN & Robust geoacoustic estimation, localization performance \(\uparrow\) & Simulated data + East & Shallow-water geoacoustic inversion and localization \\
[22] & ResNet50 & Localization error \(<\) 1 km & Simulated data + Yellow & Source localization in the Yellow Sea \\
[23] & 3D ResCNN & Accuracy of 90.5\% at 29\% sound speed mismatch & Simulated data & Deep-sea source localization \\
[24] & ConvNeXt & Prediction MSE 0.12, localization error \(<\) 5\% & Simulated data & Shallow-sea random source localization \\
[25] & CNN-CBF-MSTDE & Depth error 0.8 m vs. MSTDE & Field data & Deep-sea source depth estimation \\
[26] & UDA & Error \(\downarrow\) in low SNR/few data, better than MFP & Simulated data + field data & Few-shot localization in complex environments \\
[27] & CCNN & Deep-sea accuracy \(\uparrow\) vs. MFP & Simulated data & Low-SNR deep-sea localization \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of deep learning methods in marine environments.  
such as [PERSON] et al.'s SSLIDE method [33], [PERSON] et al.'s unsupervised range estimation [26], and [PERSON] et al.'s 3D localization [34]. The SoS dataset introduced by [PERSON] and [PERSON] [35] serves as a benchmark for indoor SSL research.