A real-life experimental study on semi-supervised source localization based on manifold regularization

Recently, we have presented a semi-supervised approach for sound source localization based on manifold regularization. The idea is to estimate the function that maps each relative transfer function (RTF) to its corresponding position. The estimation is based on an optimization problem which takes into consideration the geometric structure of the RTF samples, which is empirically deduced from prerecorded training measurements. The solution is appropriately constrained to be smooth, meaning that similar RTFs are mapped to close positions. In this paper, we conduct a comprehensive experimental study with real-life recordings to examine the algorithm performance in actual noisy and reverberant conditions. The influence of the amount of training data as well as changes in the environmental conditions are also being examined. We show that the algorithm attains accurate localization in such challenging conditions.