通过对比性学习,改善听力注意力解码在杂的环境中听力受损的听众
Gautam Sridhar1, Sofía Boselli1, Martin A Skoglund2,3
1Department of Automatic Control, Lund University, Lund, Sweden.
Journal of neural engineering
|June 9, 2025
概括
在杂的环境中,对比式学习显著增强了从电脑电图 (EEG) 数据的听觉注意力解码 (AAD). 这种神经引导的方法对听觉技术和临床应用有很大的前景.
科学领域:
- 神经科学是一个神经科学.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 听觉注意力解码 (AAD) 对于理解复杂声学环境中的语音至关重要.
- 听力障碍者需要先进的辅助技术来进行有效的听觉处理.
- 脑电图 (EEG) 提供了一种非侵入性方法来监测大脑活动.
研究的目的:
- 用EEG数据评估对比学习在改善AAD方面的有效性.
- 在具有挑战性的听觉场景中,将对比学习模型与基线和非对比模型进行比较.
- 评估基于EEG的AAD在听力技术的临床应用中的潜力.
主要方法:
- 开发了三个模型:一个基线线性模型 (LM),一个没有对比学习的非线性模型 (NLM) 和一个使用SigLIP损失进行对比学习的非线性模型 (NLMwCL).
- 来自34名听力受损的听众的EEG数据和语音信封被用于培训和评估.
- 语音信封重建精度和注意力分类精度是主要指标.
主要成果:
- 在语音重建和注意力分类方面,NLMwCL模型始终优于LM和NLM.
- 在3秒窗口内,NLMwCL实现了68.0%的注意力分类准确度,而NLM为64.4%,LM为62.6%.
- 与会语音重建的准确性在NLMwCL (0.105) 中最高,其次是NLM (0.096) 和LM (0.084).
结论:
- 对比式学习在杂,多人说话的环境中为AAD提供了显著的改进.
- 基于EEG的AAD在开发用于助听器的先进神经引导信号处理算法方面具有相当大的潜力.
- 这项研究为改善听力辅助和为有听力损失的人提供临床工具铺平了道路.
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