基于混合模型的合成声音场景中触发声音检测
Misophonia Trigger Sound Detection on Synthetic Soundscapes Using a Hybrid Model with a Frozen Pre-Trained CNN and a Time-Series Module
AI总结：
本文提出基于混合模型的合成声音场景中触发声音检测方法，利用冻结的预训练CNN和时间序列模块，通过双向GRU和ESN实现高准确率的触发声音识别，适用于个性化misophonia检测。
摘要：
misophonia是一种以对特定日常声音（触发声音）耐受性降低为特征的疾病，这些声音可能引发强烈的负面情绪反应，如愤怒、恐慌或焦虑。这些反应可能显著影响日常生活功能和生活质量。辅助技术能够选择性地检测触发声音，有助于减轻痛苦并改善福祉。在本研究中，我们研究了声音事件检测（SED）以在连续环境音频中定位触发声音的区间，作为实现此类辅助支持的基础步骤。受现实世界misophonia数据稀缺的启发，我们使用音频合成技术生成专门用于misophonia触发声音检测的合成声音场景。然后，我们使用混合CNN模型进行触发声音检测任务。这些模型结合使用冻结的预训练CNN主干进行特征提取，以及可训练的时间序列模块，如门控循环单元（GRUs）、长短期记忆（LSTMs）、回声状态网络（ESNs）及其双向变体。检测性能使用常见的SED度量标准进行评估，包括多音调声音检测得分1（PSDS1）。在多类触发SED任务中，双向时间建模始终提高检测性能，其中双向GRU（BiGRU）实现了最佳整体准确率。值得注意的是，双向ESN（BiESN）在仅优化读取时，通过减少可训练参数的量级，实现了具有竞争力的性能。我们进一步通过最多使用五个支持片段的“进食声音”检测任务来模拟用户个性化，其中BiGRU和BiESN进行了比较。在严格的适应设置中，BiESN表现出稳健和稳定的性能，表明轻量级时间模块对于个性化misophonia触发SED是很有前景的。
英文摘要：
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.
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