Sound Localization Based on Phase Difference Enhancement Using Deep Neural Networks

Abstract

The performance of most of the classical sound source localization algorithms degrades seriously in the presence of background noise or reverberation. Recently, deep neural networks (DNNs) have successfully been applied to sound source localization, which mainly aim to classify the direction-of-arrival (DoA) into one of the candidate sectors. In this paper, we propose a DNN-based phase difference enhancement for DoA estimation, which turned out to be better than the direct estimation of the DoAs from the input interchannel phase differences (IPDs). The sinusoidal functions of the phase differences for "clean and dry" source signals are estimated from the sinusoidal functions of the IPDs for the input signals, which may include directional signals, diffuse noise, and reverberation. The resulted DoA is further refined to compensate for the estimation bias near the end-fire directions. From the enhanced IPDs, we can determine the DoA for each frequency bin and the DoAs for the current frame from the distributions of the DoAs for frequencies. Experimental results with various types and levels of background noise, reverberation times, numbers of sources, room impulse responses, and DoAs showed that the proposed method outperformed conventional approaches.