A DOA estimation algorithm based on equalization-cancellation theory

Direction of arrival (DOA) estimation plays an important role in multi-channel (binaural) speech enhancement systems and auditory humanoid robots. A number of localization methods have been presented, however, most of them require a large array of microphones, or cannot adapt to some special conditions, e.g., humanoid robot with the effect of headrelated transfer function (HRTF). In this paper, we propose a two-microphone DOA estimation algorithm, namely ECBeam, which applies equalization-cancellation (EC) model to DOA estimation through beamformer-based technique. Specifically, the EC model is integrated into beamforming to remove the signal components from a given direction and yield the energy of the remaining signals from other directions. Through searching several DOA candidates in the space, the estimation of DOA is finally determined as the direction at which the energy of the remaining signal reaches the minimum. Interpolation method is further exploited in ECBeam to estimate non-beamformed directions. Experimental results showed that the EC-Beam with only two microphones is able to estimate accurately the DOA of target signal in various noise conditions, and well adapted to binaural hearing systems.