# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures
#### 3.2.4 U-Net  
The MTL-UNET-CBAM model described in [15] extends the classical U-Net architecture by integrating feature compression mechanisms and a multi-task learning framework for joint estimation of distance and depth in shallow-water underwater source localization. The network structure adopts a symmetrical encoder-decoder design, as depicted in the accompanying figure (see Figure 8).  
Encoder (Entry Flow): Each stage of the encoder employs cascaded feature compression modules with three key components:  
Dual Convolutional Layers: Utilize 3 \(\times\) 3 convolutional kernels for local feature extraction, augmented by batch normalization and parametric activation functions to strengthen nonlinear mapping capabilities.  
Attention Mechanism: Incorporates the Convolutional Block Attention Module (CBAM), which dynamically reweights critical feature channels and spatial regions through channel-wise and spatial attention mechanisms.  
Figure 8: The architecture of MTL-UNET-CBAM (b) Data transmission visualization of MTL-UNET-CBAM [15].  
Figure 7: (**a**) The structure of the FNN. (**b**) The structure of the RNN with GFGRUs. The main difference between the FNN and GFGRU lies in the hidden layer (**c**) The calculation process of the GFGRU unit [14].  
Downsampling Layer: Reduces feature map spatial dimensions using 2 \(\times\) 2 max-pooling with stride 2. This operation is mathematically expressed as  
\[F_{out}=\text{MaxPool}_{2\times 2}(\text{CBAM}(\text{ConvBlock}(\text{F}_{\text{in}}) )) \tag{12}\]  
This design reduces input dimensions from 2F \(\times\) L \(\times\) L to 1 \(\times\) F \(\times\) L\({}^{2}\), addressing the dimensional imbalance in traditional MTL-CNN.  
The U-Net framework leverages its signature encoder-decoder structure with skip connections, enhanced through multi-task learning and transfer learning strategies to achieve precise and efficient localization in challenging marine environments. Following the methodology in [15], input signals are first transformed via Fast Fourier Transform (FFT) to compute normalized covariance matrices. These matrices undergo progressive spatial reduction through two convolutional layers and 2 \(\times\) 2 max-pooling operations, preserving essential abstract features. The decoder branch subsequently processes distance and depth estimation tasks independently, with final predictions generated through fully connected layers. A key innovation lies in the integration of CBAM attention mechanisms and multi-task learning: CBAM selectively amplifies discriminative features through adaptive channel and spatial attention. Multi-task learning employs trainable weight parameters to harmonize optimization objectives across tasks.  
\[\mathcal{L}=\frac{1}{2\sigma_{r}^{2}}\left|\left|y_{r}-\widehat{y}_{r}\right| \right|^{2}+\frac{1}{2\sigma_{d}^{2}}\left|\left|\left|y_{d}-\widehat{y}_{d} \right|\right|^{2}+log\sigma_{r}\sigma_{d}\right. \tag{13}\]  
where \(\sigma_{r}^{2}\) and \(\sigma_{d}^{2}\) are adaptive task weights learned via backpropagation, balancing scale differences between km-level range and m-level depth estimations.