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

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^δτ) 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.