# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures
#### 3.2.2 Cnn  
The convolutional neural network (CNN), as a canonical feedforward deep neural network, excels in hierarchical feature extraction and parameter sharing mechanisms. As referenced [61] in the literature and illustrated in the figure, a CNN primarily consists of an  
Figure 5: The structure of LSTM [17].  
input layer, multiple feature extraction layers, fully connected layers, and an output layer. The feature extraction phase achieves progressive spatial abstraction through cascaded convolution-pooling operations, culminating in feature integration and classification by the fully connected layers. CNN's weight-sharing property significantly reduces model complexity and parameter count. Unlike traditional neural networks, convolutional kernels slide over local receptive fields of input data, extracting translation-invariant features through shared weight parameters. This mechanism not only mimics the local receptive characteristics of biological vision systems but also endows the model with strong robustness against geometric distortions such as translation, scaling, and tilting.  
Convolutional Layer: Performs local feature extraction via trainable kernels, mathematically expressed as  
\[X_{j}^{l}=f\left(\sum_{i\in M_{j}}X_{j}^{l-1}\times k_{ij}^{l}+b_{j}^{l}\right) \tag{4}\]  
\(M_{j}\) denotes the feature map selection set, \(k_{ij}^{l}\) represents convolutional kernel weights, \(b_{j}^{l}\) is the bias term, and \(f(\cdot)\) employs the ReLU activation function to enhance nonlinear expressiveness.  
Pooling Layer: Compresses feature dimensions through downsampling, serving to enhance feature spatial compactness, reduce model parameters (n \(\times\) n pooling decreases parameters by n\({}^{2}\) times), and improve noise immunity.  
\[X_{j}^{l}=f\left(\beta_{j}^{l}\cdot down\left(X_{j}^{l-1}\right)+b_{j}^{l}\right) \tag{5}\]  
Fully Connected Layer: Compresses high-dimensional features into 1D vectors and implements classification via Softmax cross-entropy loss.  
Traditional sound source localization methods rely on manually designed features and algorithms, such as DOA estimation based on time differences or phase analysis. However, these approaches often require extensive manual tuning and optimization in complex environments. In contrast, CNNs automatically learn and extract features, eliminating laborious manual feature engineering. As demonstrated in the literature, a CNN-compatible input format is constructed by converting audio signals from an 8-microphone virtual array into spectrograms via a short-time Fourier transform (STFT). These spectrograms are then concatenated across frequency bands into a 1024-dimensional input vector preserving spatial information (see Figure 6). The first four convolutional layers extract spatial features from audio spectra through local connectivity and weight sharing, while the subsequent three fully connected layers map these features to a classification space. This transforms direction estimation into a multi-class classification problem, assigning probabilities to distinct azimuthal angles for source focusing.