# Deep Learning-Based Sound Source Localization: A Review
## 2 Classification of Sound Source Localization  
### Marine Sound Source Localization  
Marine sound source localization (SSL) is crucial in various fields. Traditional methods like MFP [45; 46; 47] depend on precise models but face challenges from environmental uncertainties, especially mismatch issues due to ocean parameter variations. Improved methods like environment-focused MFP [48; 49; 50] and Bayesian MFP [51; 52] address this but raise computational load problems for real-time processing. In the domain of marine sound source localization, deep learning approaches have remarkably improved localization performance in complex environments through data-driven modeling. [PERSON] et al. proposed a CNN-based algorithm for simultaneous seabed type identification and long-range deep-sea localization, achieving a distance prediction error of \(<\)0.5 km within a 5 km range [13]. Subsequently, [PERSON] et al. integrated a CNN with beamforming, reducing localization errors by 32% in shallow-water scenarios (see Figure 2) in the South China Sea [14]. [PERSON] et al. employed a U-Net-based multi-task learning framework to compress features, enabling multi-source localization accuracy approaching that of SBL in deep-sea environments with internal waves [15]. [PERSON] et al. fused CNN and Transformer architectures, decreasing underwater ranging errors by 35% compared to traditional methods [16]. [PERSON] et al. proposed Audio Perspective Regional CNN using depthwise separable convolution, focusing on key underwater acoustic signal features via regional extraction [17]. Figure 1: (**a**) Underwater Vehicle; (**b**) Unmanned Aerial Vehicle (UAV) Platform System; (**c**) Attitude Sensing System; (**d**) Counter-UAV Demonstrator (all from German Aerospace Center, DLR). [PERSON] et al. adopted a two-stage \"beam network\" combined with data augmentation, reducing the angular root mean square error (RMSE) to 0.5\({}^{\circ}\) for direction-of-arrival (DOA) estimation in marine environments [18]. [PERSON] et al. enhanced deep-sea localization robustness via unsupervised domain adaptation, maintaining errors \(<\) 200 m under 20\({}^{\circ}\) array tilt [19]. [PERSON] et al. leveraged adversarial transfer learning with generative adversarial networks for horizontal line array localization, improving transfer accuracy by 18% to 92.7% [20]. Additionally, [PERSON] et al. optimized shallow-water geoacoustic inversion using CNN-based processing of multi-range vertical array data [21], while [PERSON] et al. employed a 50-layer residual network (ResNet50) with a two-stage training strategy, achieving localization errors \(<\) 1 km in the Yellow Sea [22]. [PERSON] et al. demonstrated 90.5% accuracy with a 3D residual CNN under 29% sound speed mismatch [23], and [PERSON] et al. combined computational mechanics and deep learning for precise shallow-water source localization [24]. [PERSON] et al. fused multi-spectral transformation with CNN, reducing deep-sea depth estimation errors to 0.8 m [25], while [PERSON] et al. outperformed traditional matched-field processing via unsupervised domain adaptation in few-shot scenarios [26]. [PERSON] et al. further improved deep-sea low-SNR localization by leveraging phase information through complex-valued convolutional networks [27]. [PERSON] et al. proposed a CNN-based method for processing multi-range vertical array data to estimate shallow water geoacoustic parameters [21]. Collectively, these studies address marine challenges such as sound speed mismatch and multipath interference through CNN variants, transfer learning, and multimodal  
Figure 2: The localization results for two simulated sources: (**a–d**) Bartlett, (**e–h**) FNN, (**i–l**) SBL, and (**m–t**) GFGRU. True positions are indicated by squares. The SBL and GFGRU provide the best performance with sidelobe suppression. The dynamic ranges of the first four rows and the last row are 10 and 80 dB, respectively (courtesy of [14]). fusion, achieving 15-60% reductions in localization errors compared to traditional methods (see Table 1).