Sound source localization based on sparse estimation and convex clustering

Sound source localization techniques using microphones have been the subject of much interest for many years. Many of them assume far-field sources, and plane waves are used as a dictionary for estimating the direction-of-arrival (DOA) of sound sources. On the other hand, there has been less research on 3D source localization which estimates both direction and distance. In case of estimating distances, monopoles must be used as a dictionary. By setting monopoles in far-field, their waves can be regarded as plane waves, and their distance can be estimated. However, monopoles set at many positions can be impossible due to high computational cost. Moreover, the grid discretization can cause estimation error because there are a lot of the number of grid points in 3D space. Such discretization issue is called off-grid problem. Therefore, a source localization with monopole-only dictionary needs some methods to solve the off-grid problem. The proposed method uses sparse estimation and modified convex clustering with a monopole-only dictionary. Sparse estimation selects the monopoles which are candidates of the source positions. Then, modified convex clustering solves the off-grid problem, and estimates source positions. In this paper, simulation and comparison with another method show effectiveness of the proposed method.