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Recent advances have shown that it is possible to identify the target speaker which a listener is attending to using single-trial EEG-based auditory attention decoding (AAD). Most AAD methods have been investigated for an open-loop scenario, where AAD is performed in an offline fashion without presenting online feedback to the listener. In this work, we aim at developing a closed-loop AAD system that allows to enhance a target speaker, suppress an interfering speaker and switch attention between both speakers. To this end, we propose a cognitive-driven adaptive gain controller (AGC) based on real-time AAD. Using the EEG responses of the listener and the speech signals of both speakers, the real-time AAD generates probabilistic attention measures, based on which the attended and the unattended speaker are identified. The AGC then amplifies the identified attended speaker and attenuates the identified unattended speaker, which are presented to the listener via loudspeakers. We investigate the performance of the proposed system in terms of the decoding performance and the signal-to-interference ratio (SIR) improvement. The experimental results show that, although there is a significant delay to detect attention switches, the proposed system is able to improve the SIR between the attended and the unattended speaker. In addition, no significant difference in decoding performance is observed between closed-loop AAD and open-loop AAD. The subjective evaluation results show that the proposed closed-loop cognitive-driven system demands a similar level of cognitive effort to follow the attended speaker, to ignore the unattended speaker and to switch attention between both speakers compared to using open-loop AAD. Closed-loop AAD in an online fashion is feasible and enables the listener to interact with the AGC.

This work focuses on daily routine recognition to personalize the hearing aid (HA) configuration for each user. So far, there is only one public data set containing the data of two acceleration sensors taken under unconstrained real-life conditions of one person. Therefore, we create a realistic and extensive data set with seven subjects and a total length of 63449 min. For the recordings, the HA streams the acceleration and audio data to a mobile phone, where the user simultaneously annotates it. This builds the grounds for our comprehensive simulations, where we train a set of classifiers in an offline and online manner to analyze the model generalization abilities across subjects for high-level activities. To achieve this, we build a feature representation, which describes the recurring daily situations and environments well. For the offline classification, the deep neural network, multi-layer perceptron (MLP), and random forest (RF) trained in a person-dependent manner show the significantly best F-measure performance of 86.6%, 87.1%, and 87.3%, respectively. We confirm that for high-level activities the person-dependent model outperforms the independent one. In our online experiments, we personalize a model that was pretrained in a person-independent manner by daily updates. Thereby, multiple incremental learners and an online RF are tested. We demonstrate that MLP and RF improve the F-measure compared to the offline baselines.

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.

This chapter contains sections titled: Introduction Wireless System for Hearing Aids Binaural Classification Systems Binaural Beamformer Blind Source Separation (BSS): An Application for a Binaural Directional Microphone Array in Hearing Aids Conclusions Bibliography