Speech enhancement in hearing aids (HAs) can take advantage of a wireless remote microphone (RM) having a better signal-to-noise ratio than the HA microphones. However, using the RM effectively is complicated by the time delay between the acoustic and wireless signals. Methods in the literature assume an instantaneous transmission of the RM signal, which is never the case in practice. Hence, we propose a practically operational method to use an RM with HAs in the presence of wireless transmission delays. Specifically, we use the delayed target voice activity state from the RM signal, to derive an expression for target speech presence probability (SPP) at the local HA microphone signals. The proposed method uses this target SPP mask as a post-filter that follows a local multichannel Wiener filter. Through simulations, we demonstrate that the proposed method improves speech quality and intelligibility metrics, especially in very noisy acoustic environments, compared to a standard approach which relies solely on HA microphones.

Most of the well-known classic beamformers have resulted from optimization problems that minimize a cost function such as the mean-square error (MSE) between the noisy speech and a reference clean speech. The rationale behind these formulations involves a speech-versus-noise dichotomy, where anything branded as noise shall be suppressed as much as possible. While leading to simple closed-form solutions and reasonably practical beamformers, this rationale has its own limitations, for instance, when the ambient noise provides context and is therefore not entirely undesirable. In this article, we offer a new rationale, where the output of the beamformer is minimally processed with respect to a certain reference signal, as long as a given performance criterion is fulfilled. We provide a case study where the performance criterion is inspired by the Speech Intelligibility Index (SII), and the processing penalty is MSE. Regarding the reference signal, we consider two cases. In the first case, the reference signal is set to the unprocessed recording from a reference microphone, giving rise to a beamformer that limits the processing of the noisy signal to a minimum necessary for fulfilling the intelligibility requirement. For the second case, the reference signal is the output of an aggressive beamformer, yielding a beamformer that essentially eliminates the noise unless the concomitant distortion of the clean speech violates the intelligibility requirement. Through simulation studies, we demonstrate some of the benefits that each of the two cases offer in relevant contexts.

One of the challenges with the implementation of multi-microphone noise reduction systems in practical applications lies in the need for the knowledge of the speech and noise covariance matrices. Recently, a method based on Maximum Likelihood (ML) estimation addressed this problem. Despite its relative success in practical setups, this method may suggest negative spectral components for the clean speech due to noise influences. In this paper, we suggest a new estimation technique that tackles this issue by enforcing a power constraint on the estimation problem. We compare the proposed method with the ML method both in synthetic and real-life scenarios using objective measures. The results suggest that the proposed method can improve speech quality without a loss of intelligibility.

In this paper, we consider the problem of estimating the target sound direction of arrival (DoA) for a hearing aid (HA) system, which can connect to a wireless microphone worn by the talker of interest. The wireless microphone “informs” the HA system about the noise-free target speech. To estimate the DoA, we consider a maximum-likelihood approach, and we assume that a database of DoA-dependent relative transfer functions (RTFs) has been measured in advance and is available. The proposed DoA estimator is able to take the available noise-free target speech, ambient noise characteristics, and the shadowing effect of the user's head on the received signals into account, and it supports both monaural and binaural microphone array configurations. Moreover, we analytically analyze the bias in the proposed estimator and introduce a modified estimator, which has been compensated for the bias. We demonstrate that the proposed method has lower computational complexity and better performance than recent RTF-based estimators. Furthermore, to decrease the number of parameters required to be wirelessly exchanged between the HAs in binaural configurations, we propose an information fusion strategy, which avoids transmitting microphone signals between the HAs. An important benefit of the proposed IF strategy is that the number of parameters to be exchanged between the HAs is independent of the number of HA microphones. Finally, we investigate the performance of variants of the proposed estimator extensively in different noisy and reverberant situations.

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.

In this paper, we propose a Direction of Arrival (DoA) estimator for a Hearing Aid System (HAS) which can connect to a wireless microphone worn by a target talker. The wireless microphone "informs" the HAS about the almost noise-free content of the target sound, and the proposed DoA estimator uses the knowledge of the noise-free target sound and the received microphone signals to estimate the DoA via a maximum likelihood approach. Moreover, the proposed DoA estimator resorts to a user-independent spherical-head model to consider the acoustic impacts of the head on the received signals at the HAS. Further, the proposed DoA estimator uses an Inverse Discrete Fourier Transform (IDFT) technique to evaluate the likelihood function computationally efficiently. We assessed the performance of the proposed estimator for various DoAs, Signal to Noise Ratios (SNRs), and target distances in different noisy and reverberant situations. The proposed estimator improves the performance markedly over other recently proposed "informed" DoA estimators.

Most state-of-the-art Sound Source Localization (SSL) algorithms have been proposed for applications which are “uninformed” about the target sound content; however, utilizing a wireless microphone worn by a target talker, enables recent Hearing Aid Systems (HASs) to access to an almost noise-free sound signal of the target talker at the HAS via the wireless connection. Therefore, in this paper, we propose a maximum likelihood (ML) approach, which we call MLSSL, to estimate the Direction of Arrival (DoA) of the target signal given access to the target signal content. Compared with other “informed” SSL algorithms which use binaural microphones for localization, MLSSL performs better using signals of one or more microphones placed on just one ear, thereby reducing the wireless transmission overhead of binaural hearing aids. More specifically, when the target location confined to the front-horizontal plane, MLSSL shows an average absolute DoA estimation error of 5 degrees at SNR of -5 dB in a large-crowd noise and non-reverberant situation. Moreover, MLSSL suffers less from front-back confusions compared with the recent approaches.

The direction dependence of Head Related Transfer Functions (HRTFs) forms the basis for HRTF-based Sound Source Localization (SSL) algorithms. In this paper, we show how spectral similarities of the HRTFs of different directions in the horizontal plane influence performance of HRTF-based SSL algorithms; the more similar the HRTFs of different angles to the HRTF of the target angle, the worse the performance. However, we also show how the microphone array geometry can assist in differentiating between the HRTFs of the different angles, thereby improving performance of HRTF-based SSL algorithms. Furthermore, to demonstrate the analysis results, we show the impact of HRTFs similarities and microphone array geometry on an exemplary HRTF-based SSL algorithm, called MLSSL. This algorithm is well-suited for this purpose as it allows to estimate the Direction-of-Arrival (DoA) of the target sound using any number of microphones and any geometries of the microphone array around the head.

We study multi-microphone noise reduction systems consisting of a beamformer and a single-channel (SC) noise reduction stage. In particular, we present and analyse a maximum likelihood (ML) method for jointly estimating the target and noise power spectral densities (psd's) entering the SC filter. We show that the estimators are minimum variance and unbiased, and provide closed-form expressions for their mean-square error (MSE). Furthermore, we show that the MSE of the noise psd estimator is particularly simple: it is independent of target signal characteristics, frequency, and microphone locations. In a hearing aid context, we analyze the performance of the estimators as a function of target angle-of-arrival and frequency. Finally, we demonstrate the advantage of the proposed method in a hearing aid situation with a target speaker in large-crowd noise.

Binary masking is a simple and efficient method for source separation, and a high increase in intelligibility can be obtained by applying the target binary mask to noisy speech. The target binary mask can only be calculated under ideal conditions and will contain errors when estimated in real-life applications. This paper proposes a method for correcting these errors. The error-correction is based on a hidden Markov model and uses the Viterbi algorithm to calculate the most probable error-free target binary mask from a target binary mask containing errors. The results demonstrate that it is possible to correct errors in the target binary mask and reduce the noise energy. However, speech energy is also reduced by the error-correction, but the impact on speech intelligibility and speech quality are not established or evaluated in the present study.

Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the assumption of at least as many sensors as sources. Further, many methods require that the number of signals within the recorded mixtures be known in advance. In many real-world applications, these limitations are too restrictive. We propose a novel method for underdetermined blind source separation using an instantaneous mixing model which assumes closely spaced microphones. Two source separation techniques have been combined, independent component analysis (ICA) and binary time - frequency (T-F) masking. By estimating binary masks from the outputs of an ICA algorithm, it is possible in an iterative way to extract basis speech signals from a convolutive mixture. The basis signals are afterwards improved by grouping similar signals. Using two microphones, we can separate, in principle, an arbitrary number of mixed speech signals. We show separation results for mixtures with as many as seven speech signals under instantaneous conditions. We also show that the proposed method is applicable to segregate speech signals under reverberant conditions, and we compare our proposed method to another state-of-the-art algorithm. The number of source signals is not assumed to be known in advance and it is possible to maintain the extracted signals as stereo signals.

When the ICA source separation problem is solved by maximum likelihood, a proper choice of the parameters is important. A comparison has been performed between the use of a mixing matrix and the use of the separation matrix as parameters in the likelihood. By looking at a general behavior of the cost function as function of the mixing matrix or as function of the separation matrix, it is explained and illustrated why it is better to select the separation matrix as a parameter than to use the mixing matrix as a parameter. The behavior of the natural gradient in the two cases has been considered as well as the influence of pre-whitening.

An online blind source separation algorithm, which is a special case of the geometric algorithm by Parra and Fancourt (Book chapt. in: Noise Reduction in Speech App., Ed. Gillian Davis, CRC Press LLC, 2002), has been implemented for the purpose of separating sounds recorded by microphones placed at each side of the head. By using the assumption that the position of the two sounds are known, the source separation algorithm has been geometrically constrained. Since the separation takes place in a non free-field, a head-related transfer function (HRTF) is used to simulate the response between microphones placed at the two ears. The use of an HRTF, instead of assuming free-field, improves the separation by approximately 1 dB, compared to when free-field is assumed. This indicates that the permutation ambiguity is solved more accurately compared to when free-field is assumed.