# Deep Learning-Based Sound Source Localization: A Review
## 2 Classification of Sound Source Localization
### Unmanned Aerial Vehicle Localization  
UAV technology has seen wide application, but illegal uses pose security risks. Traditional monitoring methods like RF [58], radar [59], and vision [60] have limitations in addressing these risks, while acoustic localization using UAV noise offers all-weather, anti-interference advantages for low-SNR monitoring. In the domain of unmanned aerial vehicle (UAV) acoustic localization, deep learning methods have particularly addressed critical challenges posed by non-stationary noise and real-time processing constraints (see Table 3). [PERSON] et al. integrated the MUSIC algorithm with deep prediction to achieve localization errors \(\leq 20\) m at \(-8\) dB SNR, outperforming traditional signal processing in low-SNR environments [36]. [PERSON] combined mel-frequency cepstral coefficients (MFCC) with 1D CNN for UAV sound recognition, achieving 96.57% precision and 92.53% recall in outdoor detection scenarios [37]. [PERSON] et al. proposed a feature-level fusion model \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Methods** & **Performances** & **Dataset** & **Scenes** \\ \hline
[32] & GCC & \begin{tabular}{c} Average azimuth error 4.6\({}^{\circ}\), \\ elevation error 3.1\({}^{\circ}\) \\ \end{tabular} & \
\begin{tabular}{c} Simulated data \\ \end{tabular} &
\begin{tabular}{c} Extreme environments \\ Indoor reverberant \\ environments \\ \end{tabular} \\
[29] & RAN & \begin{tabular}{c} Average accuracy 95.8\% \\ (far-field), 98.3\% (near-field) \\ \end{tabular} & \
\begin{tabular}{c} Simulated data + field data \\ \end{tabular} &
\begin{tabular}{c} Indoor reverberant \\ environments \\ Non-structured indoor \\ environments \\ \end{tabular} \\
[28] & CNN & \begin{tabular}{c} Accuracy 98.4\% at SNR = 10 dB, \\ T60 = 100 ms \\ \end{tabular} & \
\begin{tabular}{c} Simulated data \\ \end{tabular} &
\begin{tabular}{c} Non-structured indoor \\ environments \\ environments \\ Indoor environments \\ Indoor reverberant \\ environments \\ \end{tabular} \\
[31] & DNN & Angular accuracy 99.85\% & \
\begin{tabular}{c} Simulated data + field data \\ \end{tabular} &
\begin{tabular}{c} Indoor reverberant \\ environments \\ Dynamic reverberant \\ environments \\ \end{tabular} \\
[33] & SSLIDE & MAE 0.39 m, DOA error 8.06\({}^{\circ}\) & Simulated data + field data &
\begin{tabular}{c} Dynamic reverberant \\ environments \\ Highly reverberant \\ environments \\ \end{tabular} \\
[34] & DNN & \begin{tabular}{c} Average relative localization \\ error \(<\) 3\% \\ Amplitude features outperform \\ Mel-spectrogram and \\ TDoA features \\ \end{tabular} & \
\begin{tabular}{c} Simulated data \\ \end{tabular} &
\begin{tabular}{c} Highly reverberant \\ environments \\ Multi-scene indoor \\ localization \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of deep learning methods in indoor localization. Figure 3: (**a**) Photograph of the setup of the real-world experiments. (**b**) Photograph of the microphone array used in the experiments [31]. using Log-Mel spectrograms and MFCC, achieving 94.5% detection accuracy within 50 m by leveraging evidential theory to integrate dual-channel CNN outputs [38]. [PERSON] et al. developed a multi-scale convolutional network with global-local attention, directly processing raw audio signals to enhance UAV monitoring accuracy to 98.11% in complex environments [39]. [PERSON] et al. designed a lightweight CNN (LWCNN) for high-noise environments, combining mel-spectral features with SVM to achieve a 95.61% classification accuracy while reducing computational load [40]. [PERSON] et al. improved UAV sound event detection by integrating beamforming with CNN, expanding the effective range to 135 m despite background noise [42]. [PERSON] et al. utilized GANs to augment UAV audio datasets, improving novel UAV detection accuracy to 96.38% when combined with CNN-RNN hybrid models [41]. [PERSON] et al.