UC San Diego

Wideband source localization is an important problem in signal processing, and it has wide-range applications in underwater acoustics, indoor speaker localization, teleconferencing, and etc. Over the past few decades, there are significant amount of methods proposed for the wideband source localization. However, it still remains a challenging problem. This dissertation tackles the wideband source localization from data-driven and model-based perspectives.

For the data-driven part, a novel deep learning framework for the sound source localization (SSL) was proposed. SSL is to estimate the locations of the sound sources based on the received signal from the microphone array. SSL in the reverberant environment can be challenging due to the multipath artifacts in the received signals. To tackle with this challenge, a deep learning framework based on multi-task learning and image translation (MTIT) network is proposed. MTIT utilizes the encoder-decoder structure and it consists of one encoder and two decoders. The encoder aims to obtain a compressed representation of the input while the two decoders focus on two tasks in parallel. One decoder focuses on mitigating the multipath caused by reverberation and the other decoder predicts the source location. Due to the explicit dereverberation module and the shared encoder (representation), the proposed localization framework can achieve superior performance and can generalize to the unseen data in the reverberant environment compared to the existing baseline methods.

For the model-based part, gridless direction-of-arrival (DOA) estimation based on atomic norm minimization (ANM) for the multi-frequency signal was studied. ANM was formulated to an equivalent computationally feasible semi-definite program (SDP) problem. The dual certificate condition is given to certify the optimality. A fast algorithm implementation is given and the dual problem of the SDP is considered. The method is further generalized to the non-uniform array and non-uniform frequency case. Extensive theoretical analysis and numerical experiments demonstrate the superior performance of the proposed method compared to sparse Bayesian learning, the existing grid-based multi-frequency DOA estimation method.