EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters

Das, Neetha; Eyndhoven, Simon Van; Francart, Tom; Bertrand, Alexander

doi:10.23919/eusipco.2017.8081390

Cited by 21 publications

(36 citation statements)

References 19 publications

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“…Hence, it is essential to understand the effects of this miniaturization on EEG signal processing methods which have been tested with traditional EEG equipment. In this work, the effect of short-distance EEG measurements that arise with miniaturization of EEG sensor devices was investigated within the context of an AAD task, which may be used in future-generation neuro-steered auditory prostheses, e.g., for the cognitive control of hearing aids or cochlear implants [23], [24]. N = 2 N = 4 N = 6 N = 8 Fig.…”

Section: Discussionmentioning

confidence: 99%

Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

Narayanan

Bertrand

2019

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Objective:Concealable, miniaturized electroencephalo-graphy ('mini-EEG') recording devices are crucial enablers towards long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm. Methods: Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes which locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes, to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings. Results: The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if in both cases the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes. Conclusion: When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD. Significance: WESN-like platforms allow to achieve similar AAD performance as with long-distance EEG recordings, while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.

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Section: Discussionmentioning

confidence: 99%

Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

Narayanan

Bertrand

2019

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“…The third choice for speaker separation was 60 degrees, in which case, both the speakers were on the same side of the head. Das et al (2017) found that in a system performing neuro-steered noise suppression at low input SNRs, speaker positions on the same side of the head resulted in a relatively lower improvement in output SNRs compared to symmetric speaker set ups 1 . To balance between the two sides of the head, to avoid introducing a lateralization bias during the training of decoders (Das et al, 2016), we split this condition between two experiments where the speakers were at -30 and -90 degrees, and 30 and 90 degrees.…”

Section: Choice Of Snrsmentioning

confidence: 94%

“…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

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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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“…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

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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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EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters

Cited by 21 publications

References 19 publications

Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection

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

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