Informed Sound Source Localization Using Relative Transfer Functions for Hearing Aid Applications

Recent hearing aid systems (HASs) can connect to a wireless microphone worn by the talker of interest. This feature gives the HASs access to a noise-free version of the target signal. In this paper, we address the problem of estimating the target sound direction of arrival (DoA) for a binaural HAS given access to the noise-free content of the target signal. To estimate the DoA, we present a maximum-likelihood framework which takes the shadowing effect of the user's head on the received signals into account by modeling the relative transfer functions (RTFs) between the HAS's microphones. We propose three different RTF models which have different degrees of accuracy and individualization. Furthermore, we show that the proposed DoA estimators can be formulated in terms of inverse discrete Fourier transforms to evaluate the likelihood function computationally efficiently. We extensively assess the performance of the proposed DoA estimators for various DoAs, signal to noise ratios, and in different noisy and reverberant situations. The results show that the proposed estimators improve the performance markedly over other recently proposed “informed” DoA estimator.