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Paper Abstract and Keywords
Presentation 2026-03-03 11:40
EEG-based Auditory Attention Decoding in Diotic Listening Environments
Masahiro Yoshino, Haruki Yokota, Junya Hara, Yuichi Tanaka, Hiroshi Higashi (UOsaka) EA2025-116 SIP2025-136 SP2025-69
Abstract (in Japanese) (See Japanese page) 
(in English) Auditory attention decoding (AAD) identifies the attended speech stream in multi-speaker environments by decoding brain signals such as electroencephalography (EEG).
This technology is essential for realizing smart hearing aids that address the cocktail party problem and for facilitating objective audiometry systems.
Existing AAD research mainly utilizes dichotic environments where different speech signals are presented to the left and right ears, enabling models to classify directional attention rather than speech content. However, this spatial reliance limits applicability to real-world scenarios, such as the "cocktail party" situation, where speakers overlap or move dynamically. To address this challenge, we propose an AAD framework for diotic environments where identical speech mixtures are presented to both ears, eliminating spatial cues. Our approach maps EEG and speech signals into a shared latent space using independent encoders. We extract speech features using wav2vec 2.0 and encode them with a 2-layer 1D convolutional neural network (CNN), while employing the BrainNetwork architecture for EEG encoding. The model identifies the attended speech by calculating the cosine similarity between EEG and speech representations. We evaluate our method on a diotic EEG dataset and achieve 72.70% accuracy, which is 22.58% higher than the state-of-the-art direction-based AAD method.
Keyword (in Japanese) (See Japanese page) 
(in English) EEG / Diotic / Auditory attention decoding / Signal Processing / Deep Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 370, SIP2025-136, pp. 259-265, March 2026.
Paper # SIP2025-136 
Date of Issue 2026-02-23 (EA, SIP, SP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2025-116 SIP2025-136 SP2025-69

Conference Information
Committee SP EA SIP IPSJ-SLP  
Conference Date 2026-03-02 - 2026-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2026-03-SP-EA-SIP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) EEG-based Auditory Attention Decoding in Diotic Listening Environments 
Sub Title (in English)  
Keyword(1) EEG  
Keyword(2) Diotic  
Keyword(3) Auditory attention decoding  
Keyword(4) Signal Processing  
Keyword(5) Deep Learning  
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Keyword(7)  
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1st Author's Name Masahiro Yoshino  
1st Author's Affiliation The University of Osaka (UOsaka)
2nd Author's Name Haruki Yokota  
2nd Author's Affiliation The University of Osaka (UOsaka)
3rd Author's Name Junya Hara  
3rd Author's Affiliation The University of Osaka (UOsaka)
4th Author's Name Yuichi Tanaka  
4th Author's Affiliation The University of Osaka (UOsaka)
5th Author's Name Hiroshi Higashi  
5th Author's Affiliation The University of Osaka (UOsaka)
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Speaker Author-1 
Date Time 2026-03-03 11:40:00 
Presentation Time 20 minutes 
Registration for SIP 
Paper # EA2025-116, SIP2025-136, SP2025-69 
Volume (vol) vol.125 
Number (no) no.369(EA), no.370(SIP), no.371(SP) 
Page pp.259-265 
#Pages 7 
Date of Issue 2026-02-23 (EA, SIP, SP) 

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