An underwater sound event detection algorithm based on Temporal Graph Convolutional Network

Accurate underwater sound event detection (SED) and real-time underwater situation awareness plays an important role in intelligent sonar system. However, little research on underwater SED has been done because underwater acoustics have very complex and dynamic spectral and temporal characteristics which make underwater SED difficult to solve. The goal of SED is to recognize type and time of the input audio signal and most of SED research have been focused on airborne sound so far. To address underwater SED problem, we adopt temporal graph convolutional network (T-GCN), which is originally designed for traffic prediction and is composed of GCN and gated recurrent unit (GRU) To capture spectral and temporal information simultaneously, the GCN is used to learn topological structures to capture spectral correlation and the GRU is used to learn changes of input sound to capture temporal correlation. The proposed model utilizes sound spectrogram and its annotation with temporal information as input and then the GCN and GRU of the model is employed to solve underwater SED problem. Experiments using urban SED dataset and underwater sound dataset, demonstrate that our proposed model expresses connection of spectral and temporal information effectively and shows reliable underwater SED performance accordingly.