Supervised binaural source separation using auditory attention detection in realistic scenarios

Zakeri, Sahar; Geravanchizadeh, Masoud

doi:10.1016/j.apacoust.2020.107826

Cited by 9 publications

(14 citation statements)

References 45 publications

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“…Supervised binaural source separation using auditory attention decoding in realistic scenarios (Zakeri et al, 2021): The authors propose a complete pipeline from speech mixture to a denoised signal based on AAD. Their model attempts to separate the attended speaker from the unattended speaker in realistic scenarios, using different signal-to-noise ratios, levels of reverberation times for a simulated room, and different speaker positions.…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

Relating EEG to continuous speech using deep neural networks: a review

Puffay¹,

Accou²,

Bollens³

et al. 2023

Preprint

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Objective. When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech. Such models are limited as they assume linearity in the EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an alternative, deep learning models have recently been used to relate EEG to continuous speech, especially in auditory attention decoding (AAD) and single-speech-source paradigms. Approach. This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in AAD and single-speech-source paradigms. We point out recurrent methodological pitfalls and the need for a standard benchmark of model analysis. Main results. We gathered 28 studies. The main methodological issues we found are biased cross-validations, data leakage leading to over-fitted models, or disproportionate data size compared to the model's complexity. In addition, we address requirements for a standard benchmark model analysis, such as public datasets, common evaluation metrics, and good practices for the match-mismatch task. Significance. We are the first to present a review paper summarizing the main deep-learning-based studies that relate EEG to speech while addressing methodological pitfalls and important considerations for this newly expanding field. Our study is particularly relevant given the growing application of deep learning in EEG-speech decoding.

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Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

Relating EEG to continuous speech using deep neural networks: a review

Puffay¹,

Accou²,

Bollens³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the proposed method, the recently developed attention detection system introduced by 'O'Sullivan et al' [16], 'Lu et al' [25], 'Ciccarelli et al' [20], 'Geirnaert et al' [26], and 'Zakeri et al' [27] are simulated and used as baseline systems from the literature.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast to these approaches, the informative features technique does not require clean auditory stimuli and this characteristic makes it applicable in real-life conditions suchlike a cocktail party. Many features derived from EEG were exploited for auditory attention classification [23][24][25][26][27]. Although many researchers have introduced various features for attention detection, such features could not resolve inconsistencies or ambiguities in EEG interpretations.…”

Section: Introductionmentioning

confidence: 99%

A GRU-CNN Model for Auditory Attention Detection Using Microstate and Recurrence Quantification Analysis

EskandariNasab,

Raeisi,

Lashaki

et al. 2024

Preprint

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Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

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“…This function is very suitable for processing and predicting events with long intervals and delays in time-series. Furthermore, the classification, forecasting, signal processing, and pattern recognition for highdimensional data are prominent applications of LSTM [182], [183]. As Fig.…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%