Dynamic selective auditory attention detection using RNN and reinforcement learning
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 co...
Guardado en:
Autores principales: | , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/61a6925d5c0242f5a2e7ad00979805c7 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:61a6925d5c0242f5a2e7ad00979805c7 |
---|---|
record_format |
dspace |
spelling |
oai:doaj.org-article:61a6925d5c0242f5a2e7ad00979805c72021-12-02T16:31:52ZDynamic selective auditory attention detection using RNN and reinforcement learning10.1038/s41598-021-94876-02045-2322https://doaj.org/article/61a6925d5c0242f5a2e7ad00979805c72021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94876-0https://doaj.org/toc/2045-2322Abstract 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.Masoud GeravanchizadehHossein RoushanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
Medicine R Science Q |
spellingShingle |
Medicine R Science Q Masoud Geravanchizadeh Hossein Roushan Dynamic selective auditory attention detection using RNN and reinforcement learning |
description |
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. |
format |
article |
author |
Masoud Geravanchizadeh Hossein Roushan |
author_facet |
Masoud Geravanchizadeh Hossein Roushan |
author_sort |
Masoud Geravanchizadeh |
title |
Dynamic selective auditory attention detection using RNN and reinforcement learning |
title_short |
Dynamic selective auditory attention detection using RNN and reinforcement learning |
title_full |
Dynamic selective auditory attention detection using RNN and reinforcement learning |
title_fullStr |
Dynamic selective auditory attention detection using RNN and reinforcement learning |
title_full_unstemmed |
Dynamic selective auditory attention detection using RNN and reinforcement learning |
title_sort |
dynamic selective auditory attention detection using rnn and reinforcement learning |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/61a6925d5c0242f5a2e7ad00979805c7 |
work_keys_str_mv |
AT masoudgeravanchizadeh dynamicselectiveauditoryattentiondetectionusingrnnandreinforcementlearning AT hosseinroushan dynamicselectiveauditoryattentiondetectionusingrnnandreinforcementlearning |
_version_ |
1718383811879763968 |