The traditional eigen beam based localization algorithms are usually not employed on the non-spherical microphone array, for which the eigen beam is hard to be obtained. In this paper, the transfer functions are introduced to calculated the eigen beam on the non-spherical microphone array. Based on it, three localization algorithms including the eigen beam based intensity vector, eigen beam based beamforming, eigen beam based MUSIC, are employed and their performance on localization are evaluated.

In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localization is proposed, where the model is trained by two strategies with both cross entropy loss and mean square error loss. Based on the idea of multi-task learning, CNN is used as the shared hidden layers to extract features and DNN is used as the output layers for each task. Compared with SRP-PHAT, MUSIC and a DNN-based method, the proposed method has better performance.