Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.

Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system.

Dynamic selective auditory attention detection using RNN and reinforcement learning.

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.

Ear-EEG-based binaural speech enhancement (ee-BSE) using auditory attention detection and audiometric characteristics of hearing-impaired subjects.

. Speech perception in cocktail party scenarios has been the concern of a group of researchers who are involved with the design of hearing-aid devices..

Selective auditory attention detection based on effective connectivity by single-trial EEG.

Objective: Focusing attention on one speaker in an environment with lots of speakers is one of the important abilities of the human auditory system. The temporal dynamics of the attention process and how the brain precisely performs this task are yet unknown. This paper proposes a new method for the selective auditory attention detection (SAAD) from single-trial EEG signals using the brain effective connectivity and complex network analysis for two groups of listeners attending to the left or right ear.

Microscopic prediction of speech intelligibility in spatially distributed speech-shaped noise for normal-hearing listeners.

A binaural and psychoacoustically motivated intelligibility model, based on a well-known monaural microscopic model is proposed. This model simulates a phoneme recognition task in the presence of spatially distributed speech-shaped noise in anechoic scenarios. In the proposed model, binaural advantage effects are considered by generating a feature vector for a dynamic-time-warping speech recognizer.