Blind System Identification Using Sparse Learning for TDOA Estimation of Room Reflections
This paper presents a method to reconstruct the 3D structure of generic convex rooms from sound signals. Differently from most of the previous approaches, the method is fully uncalibrated in the sense that no knowledge about the microphones and sources position is needed. Moreover, we demonstrate that it is possible to bypass the well known echo labeling problem, allowing to reconstruct the room shape in a reasonable computation time without the need of additional hypotheses on the echoes order of arrival. Finally, the method is intrinsically robust to outliers and missing data in the echoes detection, allowing to work also in low SNR conditions. The proposed pipeline formalises the problem in different steps such as time of arrival estimation, microphones and sources localization and walls estimation. After providing a solution to these different problems we present a global optimization approach that links together all the problems in a single optimization function. The accuracy and robustness of the method is assessed on a wide set of simulated setups and in a challenging real scenario. Moreover we make freely available for a challenging dataset for 3D room reconstruction with accurate ground truth in a real scenario.