Auditory Attention Decoding without Spatial Information: A Diotic EEG Study

TL;DR

This paper introduces a novel auditory attention decoding method that works in diotic environments without spatial cues, using shared latent space mapping of EEG and speech signals, achieving significant accuracy improvements.

Contribution

It presents a new AAD framework for diotic settings that does not rely on spatial information, expanding applicability to real-world multi-speaker scenarios.

Findings

Achieved 72.70% accuracy in diotic EEG environment

Outperformed state-of-the-art direction-based AAD by 22.58%

Demonstrated effectiveness of shared latent space approach

Abstract

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.