ArticleScientific reports2021
Dynamic selective auditory attention detection using RNN and reinforcement learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.IEEE access : practical innovations, open solutions · 2025Article
- Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
- Alpha band modulation caused by selective attention to music enables EEG classification.Cognitive neurodynamics · 2024Article
- A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.Scientific reports · 2024Article
- Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network.PLoS biology · 2024Article
- Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task.Scientific reports · 2023Article
- Neural network ensemble model for prediction of erythrocyte sedimentation rate (ESR) using partial least squares regression.Scientific reports · 2022Article
- How to discern external acoustic waves in a piezoelectric neuron under noise?Journal of biological physics · 2022Article
- EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort.The European journal of neuroscience · 2022Article
- A Speech-Level-Based Segmented Model to Decode the Dynamic Auditory Attention States in the Competing Speaker Scenes.Frontiers in neuroscience · 2021Article
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Authors and funding
2 authors.
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Abstract
The 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. The proposed dynamic SAAD is modeled as a sequential decision-making problem, which is solved by recurrent neural network (RNN) and reinforcement learning methods of Q-learning and deep Q-learning. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach with RNN as agent 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.