Noise mask for TDOA sound source localization of speech on mobile robots in noisy environments

Abstract

Sound source localization is an important challenge for mobile robots operating in real life settings. Sound sources of interest, such as speech, are often corrupted by broadband coherent noise sound source(s) that are non-stationary during transitions between steady-state segments. The interfering noise introduces localization ambiguities leading to the localization of invalid sound sources. Masks to reduce such interferences perform well under stationary noise, but the performance degrades as localization of invalid sound sources generated by noise appear and disappear suddenly during transitions between steady-state. This paper presents a new mask based on speech non-stationarity to discriminate between the time difference of arrival (TDOA) of speech source and noise transition. Simulations and experiments on a mobile robot suggest that the proposed technique improve TDOA discrimination and reduces significantly localization of invalid sound sources caused by noise.