Besides reducing undesired noise sources and limiting speech distortion, another important objective of a binaural noise reduction algorithm is the preservation of the binaural cues of all sound sources in the acoustic scene. In this paper, we consider the binaural minimum variance distortionless response beamformer with partial noise estimation (BMVDR-N), which allows to trade off between noise reduction performance and binaural cue preservation of the noise component by mixing the output signals of the BMVDR beamformer with the noisy reference microphone signals. For a directional noise source, it has been shown that incorporating an external microphone in addition to the head-mounted microphones enables both the noise reduction performance as well as the interaural time and level difference cues of the noise component to be improved in the output signals. In this paper, we consider an arbitrary noise field and analytically show that incorporating an external microphone in the BMVDR-N beamformer enables 1) a larger output signal-to-noise ratio (SNR) for the same mixing parameter, 2) the same output SNR for a larger mixing parameter, and 3) the same desired output magnitude squared coherence (MSC) of the noise component for a smaller mixing parameter to be obtained. The derived analytical expressions are firstly validated using simulated anechoic acoustic transfer functions, where the listener's head is modelled as a rigid sphere. Experimental results using recorded signals for a binaural hearing device setup in a reverberant environment also show that in a realistic scenario incorporating an external microphone in the BMVDR-N beamformer significantly improves the output SNR and reduces the mixing parameter that is required to obtain a desired output MSC of the noise component compared to using only the head-mounted microphones.

During the last decades significant progress in multi-microphone speech enhancement algorithms has been made for hearing aids. However, the performance of many algorithms depends on identifying the target speaker to be enhanced. To identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers, an auditory attention decoding (AAD) method was recently proposed. This AAD method however requires the clean speech signals of both the attended and the unattended speaker as reference signals for decoding. Since in practice only microphone signals, containing several undesired acoustic components, are available, in this paper we explore the potential of using steerable binaural superdirective beamformer for generating appropriate reference signals for decoding. The experimental results show that using steerable superdirective beamformer output signals improves the decoding performance compared to using the noisy microphone signals as reference signals.

The objective of binaural speech enhancement algorithms is to reduce the undesired noise component, while preserving the desired speech source and the binaural cues of all sound sources. For the scenario of a single desired speech source in a diffuse noise field, an extension of the binaural multichannel Wiener filter (MWF), namely the MWF-IC, has been recently proposed, which aims to preserve the interaural coherence (IC) of the noise component. However, due to the large complexity of the MWF-IC, in this paper we propose several alternative algorithms at a lower computational complexity. First, we consider a quasi-distortionless version of the MWF-IC, denoted as minimum-variance-distortionless response (MVDR-IC). Second, we propose to preserve the IC of the noise component using the binaural MWF with partial noise estimation (MWF-N) and the binaural MVDR beamformer with partial noise estimation (MVDR-N), for which closed-form expressions exist. In addition, we show that for the MVDR-N a closed-form expression can be derived for the tradeoff parameter yielding a desired magnitude squared coherence (MSC) for the output noise component. Since contrary to the MWF-IC and the MWF-N the MVDR-IC and the MVDR-N do not take into account the spectro-temporal properties of the speech and the noise components, we propose to apply a spectral postfilter to the filter outputs, improving the noise reduction performance. The performance of all algorithms is compared in several diffuse noise scenarios. The simulation results show that both the MVDR-IC and the MVDR-N are able to preserve the MSC of the noise component, while generally the MVDR-IC shows a slightly better noise reduction performance at a larger complexity. Further, simulation results show that applying a spectral postfilter leads to a very similar performance for all considered algorithms in terms of noise reduction and speech distortion.

Beamforming algorithms in binaural hearing aids are crucial to improve speech understanding in background noise for hearing impaired persons. In this study, we compare and evaluate the performance of two recently proposed minimum variance (MV) beamforming approaches for binaural hearing aids. The binaural linearly constrained MV (BLCMV) beamformer applies linear constraints to maintain the target source and mitigate the interfering sources, taking into account the reverberant nature of sound propagation. The inequality constrained MV (ICMV) beamformer applies inequality constraints to maintain the target source and mitigate the interfering sources, utilizing estimates of the direction of arrivals (DOAs) of the target and interfering sources. The similarities and differences between these two approaches is discussed and the performance of both algorithms is evaluated using simulated data and using real-world recordings, particularly focusing on the robustness to estimation errors of the relative transfer functions (RTFs) and DOAs. The BLCMV achieves a good performance if the RTFs are accurately estimated while the ICMV shows a good robustness to DOA estimation errors.

Recently, an extension of the binaural multichannel Wiener filter (BMWF), referred to as BMWF-IRo, was presented in which an interference rejection constraint was added to the BMWF cost function. Although the BMWF-IRo aims to entirely suppress the interfering source, residual interfering sources (as well as unconstrained noise sources) are undesirably perceived as impinging the array from the desired source direction. In this paper, we propose two extensions of the BMWF-IRo that address this issue by preserving the spatial impression of the interfering source. In the first extension, the binaural cues of the interfering source are preserved, while those of the desired source may be slightly distorted. In the second extension, the binaural cues of both the desired and interfering sources are preserved. Simulation results show that the noise reduction performance of both proposed extensions is comparable to the BMWF-, IRo.

Besides noise reduction, an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. For the desired speech source and an interfering source, e.g., competing speaker, this can be achieved by preserving their relative transfer functions (RTFs). It has been shown that the binaural multi-channel Wiener filter (MWF) preserves the RTF of the desired speech source, but typically distorts the RTF of the interfering source. To this end, in this paper we propose an extension of the binaural MWF, i.e. the binaural MWF with RTF preservation (MWF-RTF) aiming to preserve the RTF of the interfering source. Analytical expressions for the performance of the binaural MWF and the MWF-RTF in terms of noise reduction and binaural cue preservation are derived, using which their performance is thoroughly compared. Simulation results using binaural behind-the-ear impulse responses measured in a reverberant environment validate the derived analytical expressions, showing that the MWF-RTF yields a better performance than the binaural MWF in terms of the signal-to-interference ratio and binaural cue preservation of the interfering source, while the overall noise reduction performance is slightly degraded.

In this paper we consider an acoustic scenario with a desired source and a directional interference picked up by hearing devices in a noisy and reverberant environment. We present an extension of the binaural multichannel Wiener filter (BMWF), by adding an interference rejection constraint to its cost function, in order to combine the advantages of spatial and spectral filtering while mitigating directional interferences. We prove that this algorithm can be decomposed into the binaural linearly constrained minimum variance (BLCMV) algorithm followed by a single channel Wiener post-filter. The proposed algorithm yields improved interference rejection capabilities, as compared with the BMWF. Moreover, by utilizing the spectral information on the sources, it is demonstrating better SNR measures, as compared with the BLCMV.

The objective of the speech distortion weighted multichannel Wiener filter (MWF) is to reduce background noise while controlling speech distortion. This can be achieved by means of a trade-off parameter, hence, selecting an optimal trade-off parameter is of crucial importance. Aiming at incorporating knowledge about the resulting speech distortion and noise power, in this paper we propose to compute the trade-off parameter as the point of maximum curvature of the parametric plot of noise power versus speech distortion. To determine a narrowband trade-off parameter, an analytical expression is derived for computing the point of maximum curvature, whereas to determine a broadband parameter an optimization routine is used. The speech distortion and the noise power terms can also be weighted in advance, e.g. based on perceptually motivated criteria. Experimental results show that using the proposed method instead of the MWF improves the intelligibility weighted SNR without significantly degrading the speech distortion.

Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of both desired and undesired sound sources. Recently an extension of the binaural Multi-channel Wiener filter (MWF), namely the MWF-IC, has been presented which aims to preserve the Interaural Coherence (IC) of the noise component. Since for the MWF-IC no closed-form solution exists, in this paper we propose to preserve the IC using the binaural MWF with partial noise estimation (MWF-N), for which a closed-form solution exists. Furthermore, we derive a closed-form expression for the trade-off parameter in the MWF-N yielding a predefined IC at the filter output. Experimental results in a diffuse noise scenario show that both the MWF-IC and the MWF-N preserve the IC of the output noise component. However, the MWF-IC yields a better noise reduction performance whereas the MWF-N introduces less speech distortion.

In the context of ambient assisted living, automatic speech recognition (ASR) has the potential to provide textual support for hearing aid users in challenging acoustic conditions. In this paper we therefore investigate possibilities to improve ASR based on binaural hearing aid signals in complex acoustic scenes. Particularly, information about the spatial configuration of sound sources is exploited and estimated using a recently developed method that employs probabilistic information about the location of a target speaker (and a simultaneous localized masker) for robust real-time localization. Two different strategies are investigated: straightforward better-ear listening and a multi-channel beamforming system aiming at enhancement of a target speech source with additional suppression of localized masking sound. The latter method is also complemented by better-ear listening. Both approaches are evaluated in different acoustic scenarios containing moving target and interfering speakers or noise sources. Compared to using nonpreprocessed signals, we obtain average relative reductions in word error rate of 28.4% in the presence of a localized interfering noise, 19.2% in the case of a concurrent talker and 23.7% in presence of a concurrent talker in spatially diffuse noise. A post-analysis assesses the relation of localization performance and beamforming for improved speech recognition in complex acoustic scenes.

The objective of binaural noise reduction algorithms is not only to selectively extract the desired speaker and to suppress interfering sources (e.g., competing speakers) and ambient background noise, but also to preserve the auditory impression of the complete acoustic scene. For directional sources this can be achieved by preserving the relative transfer function (RTF) which is defined as the ratio of the acoustical transfer functions relating the source and the two ears and corresponds to the binaural cues. In this paper, we theoretically analyze the performance of three algorithms that are based on the binaural minimum variance distortionless response (BMVDR) beamformer, and hence, process the desired source without distortion. The BMVDR beamformer preserves the binaural cues of the desired source but distorts the binaural cues of the interfering source. By adding an interference reduction (IR) constraint, the recently proposed BMVDR-IR beamformer is able to preserve the binaural cues of both the desired source and the interfering source. We further propose a novel algorithm for preserving the binaural cues of both the desired source and the interfering source by adding a constraint preserving the RTF of the interfering source, which will be referred to as the BMVDR-RTF beamformer. We analytically evaluate the performance in terms of binaural signal-to-interference-and-noise ratio (SINR), signal-to-interference ratio (SIR), and signal-to-noise ratio (SNR) of the three considered beamformers. It can be shown that the BMVDR-RTF beamformer outperforms the BMVDR-IR beamformer in terms of SINR and outperforms the BMVDR beamformer in terms of SIR. Among all beamformers which are distortionless with respect to the desired source and preserve the binaural cues of the interfering source, the newly proposed BMVDR-RTF beamformer is optimal in terms of SINR. Simulations using acoustic transfer functions measured on a binaural hearing aid validate our theoretical results.

Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. To this end, an extension of the binaural multi-channel Wiener filter (MWF), namely the MWF-ITF, has been proposed, which aims to preserve the Interaural Transfer Function (ITF) of the noise sources. However, the MWF-ITF is well-suited only for directional noise sources but not for, e.g., spatially isotropic noise, whose spatial characteristics cannot be properly described by the ITF but rather by the Interaural Coherence (IC). Hence, another extension of the binaural MWF, namely the MWF-IC, has been recently proposed, which aims to preserve the IC of the noise component. Since for the MWF-IC a substantial tradeoff between noise reduction and IC preservation exists, in this paper we propose a perceptually constrained version of the MWF-IC, where the amount of IC preservation is controlled based on the IC discrimination ability of the human auditory system. In addition, a theoretical analysis of the binaural cue preservation capabilities of the binaural MWF and the MWF-ITF for spatially isotropic noise fields is provided. Several simulations in diffuse noise scenarios show that the perceptually constrained MWF-IC yields a controllable preservation of the IC without significantly degrading the output SNR compared to the binaural MWF and the MWF-ITF. Furthermore, contrary to the binaural MWF and MWF-ITF, the proposed algorithm retains the spatial separation between the output speech and noise components while the binaural cues of the speech component are only slightly distorted, such that the binaural hearing advantage for speech intelligibility can still be exploited.

Besides noise reduction, an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. For the desired speech source and the interfering sources, e.g., competing speakers, this can be achieved by preserving their relative transfer functions (RTFs). It has been shown that the binaural multi-channel Wiener filter (MWF) preserves the RTF of the desired speech source, but typically distorts the RTF of the interfering sources. To this end, in this paper we propose two extensions of the binaural MWF, i.e., the binaural MWF with RTF preservation (MWF-RTF) aiming to preserve the RTF of the interfering source and the binaural MWF with interference rejection (MWF-IR) aiming to completely suppress the interfering source. Analytical expressions for the performance of the binaural MWF, MWF-RTF and MWF-IR in terms of noise reduction, speech distortion and binaural cue preservation are derived, showing that the proposed extensions yield a better performance in terms of the signal-to-interference ratio and preservation of the binaural cues of the directional interference, while the overall noise reduction performance is degraded compared to the binaural MWF. Simulation results using binaural behind-the-ear impulse responses measured in a reverberant environment validate the derived analytical expressions for the theoretically achievable performance of the binaural MWF, MWF-RTF, and MWF-IR, showing that the performance highly depends on the position of the interfering source and the number of microphones. Furthermore, the simulation results show that the MWF-RTF yields a very similar overall noise reduction performance as the binaural MWF, while preserving the binaural cues of both the speech and the interfering source.

Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of both desired and undesired sound sources. Recently, an extension of the binaural Multi-channel Wiener filter (MWF), namely the MWF-IC, has been presented, which aims to preserve the Interaural Coherence (IC) of the noise component. Since for the MWF-IC a substantial trade-off between noise reduction and IC preservation exists, in this paper we propose a perceptually constrained version of the MWF-IC, where the amount of IC preservation is controlled based on psychoacoustic criterias of the IC discrimination ability of the human auditory system. In addition, we present a simplified version of the MWF-IC, resulting in a decrease of computational complexity. Experimental results show that the perceptually motivated MWF-IC and its simplified version yield a very similar performance and the loss in intelligibility weighted output SNR compared to the binaural MWF can be limited to 0.5 dB, whereas the spatial separation between the output speech and noise component is increased leading to better perceptual results.

Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues, i.e. the Interaural Level Difference and the Interaural Time Difference of all sound sources. Recently, extensions of the binaural Multi-channel Wiener filter (MWF) have been presented, which aim to preserve the binaural cues of the residual noise component. However, since these algorithms aim to preserve the Interaural Transfer Function (ITF), they are well-suited only for directional noise sources but not for, e.g. spatially isotropic noise, which can not be fully described by the ITF. In this paper, we present an extension of the binaural MWF, aiming to preserve the Interaural Coherence of the residual noise component. Experimental results using spatially isotropic noise show that the proposed algorithm yields a good preservation of the Interaural Coherence without significantly degrading the output SNR compared to the binaural MWF and the binaural MWF with ITF preservation.

An important objective of binaural noise reduction algorithms is the preservation of the binaural cues. In this paper an extension of the Multi-channel Wiener filter with binaural cue preservation (MWF-ITF) is presented, where the average noise ITF preservation term is replaced by an instantaneous noise ITF preservation term. This framework in addition allows to impose perfect ITF preservation, leading to a hard-constraint formulation. Experimental results show that the proposed technique yields a better performance in preserving the binaural cues of both the noise component and the speech component compared to the MWF-ITF, without degrading the output SNR.