# A contrastive-learning approach for auditory attention detection

arXiv: https://arxiv.org/abs/2410.18395 · HTML: https://mlsanity.com/paper/2410.18395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices](https://mlsanity.com/paper/2008.04569/) (2020)
- [Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM Model](https://mlsanity.com/paper/2102.03957/) (2021)
- [Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning](https://mlsanity.com/paper/2308.04244/) (2023)

## Built on by (most cited first, 1 of 1)

- [NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching](https://mlsanity.com/paper/2610.00397/) (2026)

## Abstract (arXiv)

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.