A contrastive-learning approach for auditory attention detection
This addresses the challenge of isolating attended speech sources for applications like hearing aids, though it is incremental as it builds on existing EEG decoding methods.
The paper tackled the problem of detecting auditory attention from EEG signals in multi-sound environments by proposing a self-supervised contrastive-learning method, achieving state-of-the-art performance on a validation set.
Carrying conversations in multi-sound environments is one of the more challenging tasks, since the sounds overlap across time and frequency making it difficult to understand a single sound source. One proposed approach to help isolate an attended speech source is through decoding the electroencephalogram (EEG) and identifying the attended audio source using statistical or machine learning techniques. However, the limited amount of data in comparison to other machine learning problems and the distributional shift between different EEG recordings emphasizes the need for a self supervised approach that works with limited data to achieve a more robust solution. In this paper, we propose a method based on self supervised learning to minimize the difference between the latent representations of an attended speech signal and the corresponding EEG signal. This network is further finetuned for the auditory attention classification task. We compare our results with previously published methods and achieve state-of-the-art performance on the validation set.