This article addresses the problem of distance estimation using binaural hearing aid microphones in reverberant rooms. Among several distance indicators, the direct-to-reverberant energy ratio (DRR) has been shown to be more effective than other features. Therefore, we present two novel approaches to estimate the DRR of binaural signals. The first method is based on the interaural magnitude-squared coherence whereas the second approach uses stochastic maximum likelihood beamforming to estimate the power of the direct and reverberant components. The proposed DRR estimation algorithms are integrated into a distance estimation technique. When based solely on DRR, the distance estimation algorithm requires calibration where naturally the critical distance is a good calibration point. We thus propose two approaches for the calibration of the distance estimation algorithm: Informed calibration using the critical distance of the reverberant room and blind calibration using the listener's own voice. Results across various acoustical environments show the benefit of the proposed algorithms for the estimation of sound source distances up to 3 m with an estimation error of about 35 cm using informed calibration and about 1 m using the fully blind calibration strategy.

This paper proposes a novel algorithm to estimate the direct-to-reverberant energy ratio (DRR) using hearing aid microphones. The algorithm is based on the interaural magnitude-squared coherence of signals and is able to take both phase and level differences of microphones signals in the binaural configuration into account. We employ a spherical head model to approximate binaural cues. The proposed algorithm uses the common assumption of an ideally diffuse reverberation sound field. We test our approach on signals based on simulated and on measured binaural room impulse responses. Results show improved performance of the proposed algorithm as compared to other coherence-based DRR estimation methods.

In this paper we investigate two methods for the preservation of spatial cues in binaural speaker separation. We develop these methods as extensions of our previously proposed model-based generalized sidelobe canceller (GSC) which utilizes a maximum likelihood technique for speaker localization. In the proposed implementation the adaptive GSC provides an estimate of the target signal as well as an estimation of target presence probability (TPP). Binaural outputs are generated in two different ways: In the first approach the binaural signals are rendered using the GSC output signal combined with the HRTF hypotheses which are adapted by the broadband localization. The second approach uses the GSC output and the TPP to determine a common spectral postfilter. We find that the adaptive beamformer combined with the binaural rendering technique leads to larger improvements of the quality of the desired signal and delivers less unnatural fluctuations as compared to the common spectral postfilter. Informal subjective tests as well as instrumental measurements in the presence of the listener head movements reveals, however, the benefit of the spatially motivated spectral postfilter for the preservation of binaural cues of both target and interferer signals.

In this paper, we present and compare novel algorithms to localize simultaneous speakers using four microphones distributed on a pair of binaural hearing aids. The framework consists of two groups of localization algorithms, namely, beamforming-based and statistical model based localization algorithms. We first generalize our previously proposed methods based on beamforming techniques to the binaural configuration with 2 × 2 microphones. Next, we contribute two statistical model based methods for binaural localization using the maximum likelihood approach that also takes head-related transfer functions and unknown noise conditions into account. The methods enable the localization of multiple source positions for all azimuth angles and do not require prior training of binaural cues. The proposed localization algorithms are integrated into a generalized side-lobe canceller (GSC) to extract the desired speaker in the presence of competing speakers and background noise and when the head of the listener turns. The GSC components are adapted with the frequency-wise target presence probability and the frame-wise broadband direction-of-arrival (DOA) estimates that track the turns of the listener's head. We evaluate the performance of the localization algorithms individually and also in the context of the adaptive binaural beamformer in various noisy and reverberant conditions. Finally, we introduce a new adaptive beamformer, which combines the GSC with multichannel speech presence probability estimation and achieves superior source separation performance in noisy environment.

In this paper we present a novel algorithm to localize and separate simultaneous speakers using hearing aids when the head is subject to rotational movement. Most of the algorithms used in hearing aids are able to extract target signals that are in the look direction of the user and suffer from a reduced performance in localizing sounds received from other directions. Moreover, head-shadowing as well as variations like head movements may lead to significant distortions. The proposed binaural GSC beamformer includes an MMSE-based localization algorithm using an ITD/ILD model and is controlled by an inertial measurement unit. The localization algorithm can effectively localize multiple speakers in the presence of reverberation. The estimated source locations are used to adapt the GSC beamformer which extracts the desired speaker. Experimental results demonstrate the performance of the new system and especially the benefits of ILD information.

In this paper we propose a novel multi-channel algorithm to separate simultaneous speakers in an environment where the microphone array is subject to movement. When the microphones are mounted to a person's head, for instance, the movements can lead to ambiguities with respect to the sources and to distortions in the processed signal. The proposed system estimates the direction-of-arrival of the speaker's signals relative to the array and updates these estimates using an inertial measurement unit (IMU). A GMM-based localization model is used to compute the posterior probabilities of source activity in each time-frequency bin and its parameters are re-estimated during array movements. Then, a model-based generalized side-lobe canceler (GSC) whose components are continuously updated, is employed for the separation of sources. For various speeds of microphone array rotation, it is demonstrated that the IMU-based system delivers improved speech quality when compared to the baseline technique without IMU.