Bi-Direction Interaural Matching Filter and Decision Weighting Fusion for Sound Source Localization in Noisy Environments

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter ( IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori ( MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.National Natural Science Foundation of China (NSFC) [61340046, 60875050, 60675025]; National High Technology Research and Development Program of China (863 Program) [2006AA04Z247]; Specialized Research Fund for the Doctoral Program of Higher Education [20130001110011]; Natural Science Foundation of Guangdong Province [2015A030311034]; Science and Technology Innovation Commission of Shenzhen Municipality [JCYJ20130331144631730, JCYJ20130331144716089]SCI(E)[email protected]