# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
###### Abstract  
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. auditory attention decoding; adaptive gain control; speech enhancement; EEG; brain computer interface +
Footnote †: journal: _Appl. of the Acoustics and  
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methods are based on, for example, a least-squares cost function [3; 4; 5; 6; 7; 11; 14; 16], canonical correlation analysis [8; 13], a state-space model [9; 18], and neural networks [12; 15; 17]. The least-squares-based AAD method used in Reference [3; 5; 6; 7; 11; 14] aims at reconstructing the attended speech envelope from the EEG responses of the listener using a trained spatio-temporal envelope estimator. To identify the attended speaker, the reconstructed speech envelope is compared with the speech envelopes of the competing speakers using correlation coefficients. Since these correlation coefficients are typically highly fluctuating, a large correlation window of about 30 s is typically required to obtain a reliable decoding performance, which causes a large processing delay [9; 18; 19]. The possibility of decoding auditory attention from EEG recordings has led to an increasing research interest in the topic of incorporating AAD in a brain-computer interface for real-world applications, for example, to cognitively drive speech enhancement algorithms [19; 20; 21; 22; 23; 24; 25]. Cognitive-driven speech enhancement algorithms potentially provide the listener with the ability to selectively attend to a specific speaker. It should, however, be noted that the performance of most aforementioned AAD methods and cognitive-driven speech enhancement algorithms has been investigated for an open-loop scenario, where AAD is performed in an offline fashion without presenting online feedback to the listener. In addition, scenarios with no attention switch between speakers have typically been investigated, which is unrealistic in practice. To investigate the performance of AAD for real-world applications, closing the loop by presenting feedback according to the AAD results in an online fashion is of crucial importance. Feedback presentation may influence the subsequent intent of the listener and the brain signals that encode that intent.