EEG-Based Auditory Attention Decoding Using Steerable Binaural Superdirective Beamformer

Aroudi, Ali; Marquardt, Daniel; Daclo, Simon

doi:10.1109/icassp.2018.8462278

Cited by 13 publications

(14 citation statements)

References 16 publications

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“…In this study we used the clean speech envelopes, which in a real-life scenario will not be available, for decoding. Many algorithms have been proposed to cope with realistic microphone signals (Aroudi et al, 2018;Das et al, 2017;O'Sullivan et al, 2017;Van Eyndhoven et al, 2017), where it was found that the attention decoding performance was slightly worse than with clean speech envelopes. Therefore, the current results should be considered optimistic in this respect.…”

Section: Road To Neuro-steered Hearing Devicesmentioning

confidence: 99%

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

Das

Bertrand

Francart

2018

Preprint

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Objective. A listener's neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment. Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listener's attention. A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy. Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively. The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy. Approach. 28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from 2 different locations, along with surrounding multitalker background babble. EEG signals of the subjects were recorded while they focused on one story and ignored the other. The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations. Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30s segment of data. Behavioral speech recognition thresholds were obtained for the different speaker separations. Main results. Both the background noise level and the angular separation between speakers affected attention decoding accuracy. Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level. We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking. Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased. A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions. Significance. This work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy. It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions. It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses.

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Section: Road To Neuro-steered Hearing Devicesmentioning

confidence: 99%

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

Das

Bertrand

Francart

2018

Preprint

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“…In all these AAD approaches, access to clean speech streams is necessary. Therefore some integrated demixing and noise suppression algorithms have been developed to grant access to clean speech streams (Aroudi et al, 2018;Das et al, 2017;O'Sullivan et al, 2017;Van Eyndhoven et al, 2017). Researchers have optimized the number and location of concealable miniature EEG electrodes for wearability purposes, minimizing the subsequent loss in performance (Fiedler et al, 2016;Mirkovic et al, 2015;Narayanan Mundanad and Bertrand, 2018).…”

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

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When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

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“…Multichannel enhancement via multimicrophone beamforming (e.g., Aroudi et al 2018 ; Moore et al 2018 , 2019 ) and deep learning (e.g., Chen et al 2016 , 2017 ; Wang & Chen 2018 ) have been suggested to capture and enhance the signals. The glasses form factor allows for multichannel speech enhancement, where improvements in the SNR can be on the order of 10 to 20 dB under certain circumstances (see Doclo, 2003 ).…”

Section: Ar: Hearing-enhancing Devicesmentioning

confidence: 99%

Potential of Augmented Reality Platforms to Improve Individual Hearing Aids and to Support More Ecologically Valid Research

et al. 2020

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An augmented reality (AR) platform combines several technologies in a system that can render individual “digital objects” that can be manipulated for a given purpose. In the audio domain, these may, for example, be generated by speaker separation, noise suppression, and signal enhancement. Access to the “digital objects” could be used to augment auditory objects that the user wants to hear better. Such AR platforms in conjunction with traditional hearing aids may contribute to closing the gap for people with hearing loss through multimodal sensor integration, leveraging extensive current artificial intelligence research, and machine-learning frameworks. This could take the form of an attention-driven signal enhancement and noise suppression platform, together with context awareness, which would improve the interpersonal communication experience in complex real-life situations. In that sense, an AR platform could serve as a frontend to current and future hearing solutions. The AR device would enhance the signals to be attended, but the hearing amplification would still be handled by hearing aids. In this article, suggestions are made about why AR platforms may offer ideal affordances to compensate for hearing loss, and how research-focused AR platforms could help toward better understanding of the role of hearing in everyday life.

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EEG-Based Auditory Attention Decoding Using Steerable Binaural Superdirective Beamformer

Cited by 13 publications

References 16 publications

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

EEG-based detection of the locus of auditory attention with convolutional neural networks

Potential of Augmented Reality Platforms to Improve Individual Hearing Aids and to Support More Ecologically Valid Research

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