为神经多样性人群设计低延迟音频增强，过滤引发不适的特定声音。
Low-latency Assistive Audio Enhancement for Neurodivergent People
- 基于社区调研筛选触发音，构建专用音效数据集
- 动态范围压缩（DRC）效果最佳，显著降低听觉不适感
- 适合需要实时降噪的自闭症、感官敏感者使用
神经多样性人群常面临听觉敏感问题，估计有50%-70%受到影响。这种敏感可能引发从轻微不适到严重焦虑的反应，凸显了辅助音频增强技术的迫切需求。本文提出多种辅助音频增强算法，旨在选择性过滤令人不适的声音。通过分析Reddit等平台上的神经多样性相关社区，我们整理出潜在触发声音清单，并从FSD50K和ESC50等公开数据源采集样本，构建了触发音效数据集。利用该数据集，训练并评估了多种数字信号处理（DSP）与机器学习（ML）音频增强方法。实验表明，动态范围压缩（DRC）在降低触发声音强度方面表现最优，能有效减轻神经多样性人群的听觉困扰。
原文摘要 · Abstract (English)
Neurodivergent people frequently experience decreased sound tolerance, with estimates suggesting it affects 50-70% of this population. This heightened sensitivity can provoke reactions ranging from mild discomfort to severe distress, highlighting the critical need for assistive audio enhancement technologies In this paper, we propose several assistive audio enhancement algorithms designed to selectively filter distressing sounds. To address this, we curated a list of potential trigger sounds by analyzing neurodivergent-focused communities on platforms such as Reddit. Using this list, a dataset of trigger sound samples was compiled from publicly available sources, including FSD50K and ESC50. These samples were then used to train and evaluate various Digital Signal Processing (DSP) and Machine Learning (ML) audio enhancement algorithms. Among the approaches explored, Dynamic Range Compression (DRC) proved the most effective, successfully attenuating trigger sounds and reducing auditory distress for neurodivergent listeners.
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