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

(17 citation statements)

References 45 publications

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“…Figure 9 compares the performances of the proposed AAD method based on the optimal feature set (i.e., mean GFP + RR") and GCQL classifier beside the baseline systems in terms of ACC measures. According to the accuracy criteria, the introduced AAD algorithm has superior proficiency than the baseline systems including "O'Sullivan et al [16]", "Lu et al [25]", "Ciccarelli et al [20]", "Geirnaert et al [26]", and 'Zakeri et al' [27]. It is observed that the accuracy of the baseline systems is increased with the increasing length of EEG durations, in general.…”

Section: Discussionmentioning

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%

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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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Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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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%

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh

Mohammadi‐Ivatloo

Pourhossein

et al. 2022

IEEE Trans. Power Electron.

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“…Bi-LSTM, as an extension of the traditional LSTM [33], is trained on the input sequence, with two LSTMs set up in reverse order (see Figure 1). The LSTM layer reduces the vanishing gradient problem and allows the use of deeper networks compared with recurrent neural networks (RNNs) [34,35]. In the structure of the traditional RNN and the LSTM model, the propagation of information happens in a forward path, in which case the time depends only on the information before the time .…”

Section: Bidirectional Long Short-term Memory (Bi-lstm)mentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

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Supervised binaural source separation using auditory attention detection in realistic scenarios

Cited by 9 publications

References 45 publications

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

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

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

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