APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 3

EEG-based Auditory Attention Detection in Cocktail Party Environment

Siqi Cai, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Hongxu Zhu, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Tanja Schultz, Cognitive Systems Lab, University of Bremen, Germany, Haizhou Li, Shenzhen Research Institute of Big Data, School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China and Machine Listening Lab, University of Bremen, Germany, haizhouli@cuhk.edu.cn
 
Suggested Citation
Siqi Cai, Hongxu Zhu, Tanja Schultz and Haizhou Li (2023), "EEG-based Auditory Attention Detection in Cocktail Party Environment", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 3, e22. http://dx.doi.org/10.1561/116.00000128

Publication Date: 02 Oct 2023
© 2023 S. Cai, H. Zhu, T. Schultz and H. Li
 
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In this article:
Introduction 
Fundamental of the Speech Perception 
Speech Reconstruction from Brain Signal 
Typical EEG-based Auditory Attention Detection 
Deep Learning Approaches 
Towards Neuro-steered Hearing Devices 
Datasets for Auditory Attention Detection Study 
Challenges and Directions 
Conclusion 
References 

Abstract

The cocktail party effect refers to a challenging problem in speech perception where one is able to selectively attend to one sound source in a noisy and multi-talk environment. The recent studies in neuroscience and psychoacoustics shed light on how the human brain solves the cocktail party problem, that inspires many computational solutions. With the advent of novel physiological techniques and deep learning algorithms, it is now possible to effectively detect auditory attention based on brain signals. In this paper, we provide a comprehensive overview of the most recent EEG-based auditory attention detection techniques and the methods to evaluate their performance. We examine both statistical and deep learning approaches, exploring their strengths and limitations. Furthermore, we also point out the gaps between the state-of-the-art and the practical needs in real-world applications. We also offer an overview of the available resources for EEG-based auditory attention detection research.

DOI:10.1561/116.00000128

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APSIPA Transactions on Signal and Information Processing Special Issue - Advanced Acoustic, Sound and Audio Processing Techniques and Their Applications
See the other articles that are part of this special issue.