Abstract
Misophonia is a disorder characterized by a decreased tolerance to specific everyday sounds (trigger sounds) that can evoke intense negative emotional responses such as anger, panic, or anxiety. These reactions can substantially impair daily functioning and quality of life. Assistive technologies that selectively detect trigger sounds could help reduce distress and improve well-being. In this study, we investigate sound event detection (SED) to localize intervals of trigger sounds in continuous environmental audio as a foundational step toward such assistive support. Motivated by the scarcity of real-world misophonia data, we generate synthetic soundscapes tailored to misophonia trigger sound detection using audio synthesis techniques. Then, we perform trigger sound detection tasks using hybrid CNN-based models. The models combine feature extraction using a frozen pre-trained CNN backbone with a trainable time-series module such as gated recurrent units (GRUs), long short-term memories (LSTMs), echo state networks (ESNs), and their bidirectional variants. The detection performance is evaluated using common SED metrics, including Polyphonic Sound Detection Score 1 (PSDS1). On the multi-class trigger SED task, bidirectional temporal modeling consistently improves detection performance, with Bidirectional GRU (BiGRU) achieving the best overall accuracy. Notably, the Bidirectional ESN (BiESN) attains competitive performance while requiring orders of magnitude fewer trainable parameters by optimizing only the readout. We further simulate user personalization via a few-shot "eating sound" detection task with at most five support clips, in which BiGRU and BiESN are compared. In this strict adaptation setting, BiESN shows robust and stable performance, suggesting that lightweight temporal modules are promising for personalized misophonia trigger SED.
Abstract (translated)
口恶声症（Misophonia）是一种障碍，其特点是对于特定的日常声音（触发声音）容忍度降低，这些声音能够引发强烈的负面情绪反应如愤怒、恐慌或焦虑。这些反应会严重影响日常生活和生活质量。选择性检测触发声音的辅助技术可以帮助减少不适感并提高幸福感。在这项研究中，我们探讨了声学事件检测（SED），以在连续环境音频中定位触发声音的时间段，作为提供此类支持的基础步骤。 由于现实世界中的口恶声症数据稀缺，我们使用音频合成技术生成针对口恶声症触发声音检测的合成音景。然后，我们使用基于混合卷积神经网络（CNN）的模型执行触发声音检测任务。这些模型结合了使用冻结预训练的CNN主干进行特征提取与可训练的时间序列模块（如门控循环单元GRUs、长短时记忆LSTMs以及回声状态网络ESNs及其双向变体）。 检测性能通过常见的SED度量评估，包括复音声音检测得分1（PSDS1）。在多类触发声学事件检测任务中，双向时间建模一致提高了检测性能。其中，双向GRU（BiGRU）实现了最佳整体准确性。值得注意的是，优化仅读出部分的双向ESN（BiESN）获得了竞争性表现，同时需要数个量级更少的可训练参数。 我们进一步通过一个最多五段支持片段的小样本“进食声音”检测任务模拟用户个性化设置，在这种严格的适应环境设定下，BiGRU和BiESN进行比较。在此环境下，BiESN展示了稳健且稳定的性能，表明轻量级时间模块对于个性化的口恶声症触发声学事件检测具有前景。
URL
https://arxiv.org/abs/2602.06271
