Estimation of direction of arrival using information theory

Abstract

Abstract-Estimating the direction of arrival (DOA) of an acoustic source relies on the successful estimation of the relative delay between pairs of microphone signals. Processing is performed at the current time by operating on blocks of recorded data. When these recordings are performed in environments of strong multipath reflections, algorithms often fail to distinguish between the true DOA and that of a dominant reflection. In this letter, we assume Gaussianity of the source signal and use an information-theoretical measure, often met in blind source separation algorithms, to derive a robust DOA estimator, even under significant reverberant conditions. We discuss the most popular algorithm for time delay estimation, namely, the generalized cross-correlation method, and demonstrate under certain conditions its connection to the proposed one. Performance is demonstrated for both algorithms with sets of simulated results as a function of different reverberation times, microphone spacing, and data block size. The results indicate that the examined framework can accurately track the DOA of a typical acoustic source.

Robust time delay estimation for speech signals using information theory: A comparison study

“…It is shown in [11] that the estimator searching the relative delay between two microphone signals by directly maximizing the MI suffers from the same limitations of GCC, and it is not robust enough in reverberant acoustic environments.…”

“…However, for nonGaussian source signals, higher order statistics (HOS) have more to say about their dependence. More recently, the two information-theoretic concepts of joint entropy and mutual information (MI), which can be considered as higher order statistics [9], are used to develop new TDE estimators [10,11]. In [10], the Laplacian is employed to model the speech source, and the relative delay is estimated via minimizing the joint entropy of the multiple microphone output signals.…”

“…In [10], the Laplacian is employed to model the speech source, and the relative delay is estimated via minimizing the joint entropy of the multiple microphone output signals. In [11], based on characterizing the speech source as Gaussian, the MI measure is used for TDE, however, the method is restricted to the two microphone case.…”

“…Analysing further the work of [10,11], in this article, we present a framework that treats the TDE problem from an information theory point-of-view. Since the two information-theoretic measures have the freedom of selecting a specific distribution model for the source signal, the solutions based on minimizing the joint entropy and maximizing the MI of the multichannel output signals are derived for both Gaussian and Laplacian models.…”

A Modified SRP-PHAT Functional for Robust Real-Time Sound Source Localization With Scalable Spatial Sampling

“…On the other hand, direct approaches perform TDOA estimation and source localization in one single step by scanning a set of candidate source locations and selecting the most likely position as an estimate of the source location. In addition, information theoretic approaches have also shown to be significantly powerful in source localization tasks [3].…”