Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy.

Indoor sound source localization is a key technique in many engineering applications, and an inverse element-free method based on joint sparse recovery in a Bayesian framework is proposed for reverberant environments. In this method, a discrete wave model is constructed to represent the relationships between the sampled sound pressure and the source intensity distribution, and localization in the reverberant environment is realized via inversion from the wave model. By constructing a compact supporting domain, the source intensity can be sparsely represented in subdomains, and the sparse Bayesian framework is used to recover the source intensity. In particular, joint sparse recovery in the frequency domain is exploited to improve the recovery performance. Numerical and experimental verifications show that, compared with another state-of-the-art method, the proposed method achieves high source-localization accuracy and low sidelobes with low computational complexity in highly reverberant environments.