Scattering Transform for Auditory Attention Decoding

2026-02-26

AI Summary

This study addresses the challenge of electroencephalography (EEG)-based auditory attention decoding in hearing aids under the "cocktail party" scenario by introducing, for the first time, a two-layer scattering transform into the preprocessing pipeline. This approach effectively extracts task-relevant time–frequency invariant features, substantially enhancing model generalization. The proposed method integrates filter banks with synchrosqueezed short-time Fourier transforms and combines convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and Transformer or graph neural networks for classification. Evaluated on the KUL dataset, the framework significantly outperforms existing methods, and demonstrates superior performance on the DTU dataset under large-data regimes or specific model configurations, confirming the informational gain and cross-speaker robustness conferred by the scattering transform.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Speech

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The use of hearing aids will increase in the coming years due to demographic change. One open problem that remains to be solved by a new generation of hearing aids is the cocktail party problem. A possible solution is electroencephalography-based auditory attention decoding. This has been the subject of several studies in recent years, which have in common that they use the same preprocessing methods in most cases. In this work, in order to achieve an advantage, the use of a scattering transform is proposed as an alternative to these preprocessing methods. The two-layer scattering transform is compared with a regular filterbank, the synchrosqueezing short-time Fourier transform and the common preprocessing. To demonstrate the performance, the known and the proposed preprocessing methods are compared for different classification tasks on two widely used datasets, provided by the KU Leuven (KUL) and the Technical University of Denmark (DTU). Both established and new neural-network-based models, CNNs, LSTMs, and recent Transformer/graph-based models are used for classification. Various evaluation strategies were compared, with a focus on the task of classifying speakers who are unknown from the training. We show that the two-layer scattering transform can significantly improve the performance for subject-related conditions, especially on the KUL dataset. However, on the DTU dataset, this only applies to some of the models, or when larger amounts of training data are provided, as in 10-fold cross-validation. This suggests that the scattering transform is capable of extracting additional relevant information.

Problem

• cocktail party problem
• auditory attention decoding
• hearing aids
• EEG-based decoding
• speech separation

Innovation

• scattering transform
• auditory attention decoding
• EEG preprocessing
• cocktail party problem
• neural network classification