Biomimetic direction of arrival estimation for resolving front-back confusions in hearing aids

Sound sources at the same angle in front or behind a two-microphone array (e.g., bilateral hearing aids) produce the same time delay and two estimates for the direction of arrival: A front-back confusion. The auditory system can resolve this issue using head movements. To resolve front-back confusion for hearing-aid algorithms, head movement was measured using an inertial sensor. Successive time-delay estimates between the microphones are shifted clockwise and counterclockwise by the head movement between estimates and aggregated in two histograms. The histogram with the largest peak after multiple estimates predicted the correct hemifield for the source, eliminating the front-back confusions

Approximate maximum likelihood estimation of two closely spaced sources

The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

A probabilistic method for blind multiuser detection using array observations

In this paper, a blind algorithm for detecting active users in a DS-CDMA system is presented. This probabilistic algorithm relies on the theory of hidden Markov models (HMM) and is completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. Additionally, observation through an array of sensors is also considered. Performance is verified via computer simulations, showing the near-far resistance of the analyzed procedure.Peer ReviewedPostprint (published version