A two-layer probabilistic model based on time-delay compensation for binaural sound localization

Abstract

Interaural Intensity Difference (IID) and Interaural Time Difference (ITD) are two important cues for robot acoustic localization both in Artificial Intelligence (AI) and Human-Robot Interaction (HRI) areas. However, it is a challenge job to localize a sound source accurately and swiftly only by two acoustic sensors. In this paper, a time-delay compensation based two-layer probabilistic model is presented for binaural sound source localization. In the first layer, a weighting function of Generalized Cross Correlation (GCC) named PHAT-ργ is used in low-frequency to obtain the prior time-delay. And in this layer a crude estimate of azimuth can also be acquired. At the same time, the probability of all possible time-delay lags can be achieved from the training data. In the Second layer, a new improved algorithm of IID based on time-delay compensation(named IID <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δτ</sup> ) is introduced to refine the probability of the azimuth and the elevation. Lastly, localization result is obtained by Bayes-Rule method. Comparing with three state-of-art algorithms, experimental results show that the proposed method has higher accuracy and costs less time for sound source localization.