scholarly journals Comparison of speech envelope extraction methods for EEG-based auditory attention detection in a cocktail party scenario

2015 ◽  
Author(s):  
Wouter Biesmans ◽  
Jonas Vanthornhout ◽  
Jan Wouters ◽  
Marc Moonen ◽  
Tom Francart ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Auditory Attention ◽  
Cocktail Party ◽  
Envelope Extraction ◽  
Speech Envelope

scholarly journals The Spatial Selective Auditory Attention of Cochlear Implant Users in Different Conversational Sound Levels

2021 ◽  
Vol 10 (14) ◽  
pp. 3078
Author(s):  
Sara Akbarzadeh ◽  
Sungmin Lee ◽  
Chin-Tuan Tan
Keyword(s):  
Cochlear Implant ◽  
Sound Level ◽  
Auditory Attention ◽  
Impaired Hearing ◽  
Speech Stimuli ◽  
Electric Hearing ◽  
Sound Levels ◽  
Acoustic Hearing ◽  
Speech Envelope ◽  
Different Levels

In multi-speaker environments, cochlear implant (CI) users may attend to a target sound source in a different manner from normal hearing (NH) individuals during a conversation. This study attempted to investigate the effect of conversational sound levels on the mechanisms adopted by CI and NH listeners in selective auditory attention and how it affects their daily conversation. Nine CI users (five bilateral, three unilateral, and one bimodal) and eight NH listeners participated in this study. The behavioral speech recognition scores were collected using a matrix sentences test, and neural tracking to speech envelope was recorded using electroencephalography (EEG). Speech stimuli were presented at three different levels (75, 65, and 55 dB SPL) in the presence of two maskers from three spatially separated speakers. Different combinations of assisted/impaired hearing modes were evaluated for CI users, and the outcomes were analyzed in three categories: electric hearing only, acoustic hearing only, and electric + acoustic hearing. Our results showed that increasing the conversational sound level degraded the selective auditory attention in electrical hearing. On the other hand, increasing the sound level improved the selective auditory attention for the acoustic hearing group. In the NH listeners, however, increasing the sound level did not cause a significant change in the auditory attention. Our result implies that the effect of the sound level on selective auditory attention varies depending on the hearing modes, and the loudness control is necessary for the ease of attending to the conversation by CI users.

scholarly journals An fMRI Study to Investigate Auditory Attention: A Model of the Cocktail Party Phenomenon

2005 ◽  
Vol 4 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Toshiharu NAKAI ◽  
Chikako KATO ◽  
Kayako MATSUO
Keyword(s):  
Auditory Attention ◽  
Cocktail Party ◽  
Fmri Study

The Combination of Neural Tracking and Alpha Power Lateralization for Auditory Attention Detection

2021 ◽  
pp. 1-14
Author(s):  
Szymon Drgas ◽  
Magdalena Blaszak ◽  
Anna Przekoracka-Krawczyk
Keyword(s):  
Auditory Attention ◽  
Natural Speech ◽  
Alpha Power ◽  
Cocktail Party ◽  
Combined Method ◽  
Acoustic Source ◽  
Accurate Identification ◽  
Speech Stimuli ◽  
Listening Task ◽  
Similar Accuracy

Purpose The acoustic source that is attended to by the listener in a mixture can be identified with a certain accuracy on the basis of their neural response recorded during listening, and various phenomena may be used to detect attention. For example, neural tracking (NT) and alpha power lateralization (APL) may be utilized in order to obtain information concerning attention. However, these methods of auditory attention detection (AAD) are typically tested in different experimental setups, which makes it impossible to compare their accuracy. The aim of this study is to compare the accuracy of AAD based on NT, APL, and their combination for a dichotic natural speech listening task. Method Thirteen adult listeners were presented with dichotic speech stimuli and instructed to attend to one of them. Electroencephalogram of the subjects was continuously recorded during the experiment using a set of 32 active electrodes. The accuracy of AAD was evaluated for trial lengths of 50, 25, and 12.5 s. AAD was tested for various parameters of NT- and APL-based modules. Results The obtained results suggest that NT of natural running speech provides similar accuracy to APL. The statistically significant improvement of the accuracy of AAD using a combined method has been observed not only for the longest duration of test samples (50 s, p = .005) but also for shorter ones (25 s, p = .011). Conclusions It seems that the combination of standard NT and APL significantly increases the effectiveness of accurate identification of the traced signal perceived by a listener under dichotic conditions. It has been demonstrated that, under certain conditions, the combination of NT and APL may provide a benefit for AAD in cocktail party scenarios.

scholarly journals Selective auditory attention detection using dynamic learning systems: The study of RNN and reinforcement learning

2021 ◽  
Author(s):  
Masoud Geravanchizadeh ◽  
Hossein Roushan
Keyword(s):  
Reinforcement Learning ◽  
Detection System ◽  
Auditory Attention ◽  
Final Decision ◽  
Learning Approaches ◽  
Cocktail Party ◽  
Dynamic Learning ◽  
Learning Stage ◽  
Q Learning ◽  
Markov Decision

AbstractThe cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. In the proposed dynamic system, after preprocessing of the input signals, the probabilistic state space of the system is formed. Then, in the learning stage, different dynamic learning methods, including recurrent neural network (RNN) and reinforcement learning (Markov decision process (MDP) and deep Q-learning) are applied to make the final decision as to the attended speech. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach (MDP+RNN) provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.

scholarly journals Impact of Different Acoustic Components on EEG-based Auditory Attention Decoding in Noisy and Reverberant Conditions

2018 ◽  
Author(s):  
Ali Aroudi ◽  
Bojana Mirkovic ◽  
Maarten De Vos ◽  
Simon Doclo
Keyword(s):  
Background Noise ◽  
Auditory Attention ◽  
Single Trial ◽  
Reference Signals ◽  
Temporal Filter ◽  
Spatio Temporal ◽  
Eeg Recordings ◽  
Speech Envelope ◽  
Acoustic Conditions ◽  
The Impact

AbstractRecently, a least-squares-based method has been proposed to decode auditory attention from single-trial EEG recordings for an acoustic scenario with two competing speakers. This method aims at reconstructing the attended speech envelope from the EEG recordings using a trained spatio-temporal filter. While the performance of this method has been mainly studied for noiseless and anechoic acoustic conditions, it is important to fully understand its performance in realistic noisy and reverberant acoustic conditions. In this paper, we investigate auditory attention decoding (AAD) using EEG recordings for different acoustic conditions (anechoic, reverberant, noisy, and reverberant-noisy). In particular, we investigate the impact of different acoustic conditions for AAD filter training and for decoding. In addition, we investigate the influence on the decoding performance of the different acoustic components (i.e. reverberation, background noise and interfering speaker) in the reference signals used for decoding and the training signals used for computing the filters. First, we found that for all considered acoustic conditions it is possible to decode auditory attention with a decoding performance larger than 90%, even when the acoustic conditions for AAD filter training and for decoding are different. Second, when using reference signals affected by reverberation and/or background noise, a comparable decoding performance as when using clean reference signals can be obtained. In contrast, when using reference signals affected by the interfering speaker, the decoding performance significantly decreases. Third, the experimental results indicate that it is even feasible to use training signals affected by reverberation, background noise and/or the interfering speaker for computing the filters.

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

2018 ◽  
Author(s):  
Neetha Das ◽  
Alexander Bertrand ◽  
Tom Francart
Keyword(s):  
Boundary Conditions ◽  
Noise Level ◽  
Background Noise ◽  
Noise Suppression ◽  
Auditory Attention ◽  
Cocktail Party ◽  
Background Noise Level ◽  
Speaker Separation ◽  
Acoustic Conditions ◽  
Decoding Accuracy

AbstractObjectiveA 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.Approach28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from 2 different locations, along with surrounding multi-talker 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 resultsBoth 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.SignificanceThis 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.Index TermsAuditory attention detection, EEG processing, neuro-steered auditory prostheses, brain-computer interface, cocktail party, acoustic conditions.The work is funded by KU Leuven Special Research Fund C14/16/057 and OT/14/119, FWO project nrs. 1.5.123.16N and G0A4918N, the ERC (637424) under the European Union’s Horizon 2020 research and innovation programme, and a research gift of Starkey Hearing Technologies. The scientific responsibility is assumed by its authors.

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