用二维情绪空间分析狗叫，让助犬更准识别癫痫预警
A Dimensional Approach to Canine Bark Analysis for Assistance Dog Seizure Signaling
- 将狗叫分析转为连续回归任务，基于情绪高低与兴奋度建模
- 在难测的效价维度上，误判率降低50%比基准模型更准
- 适合数据少、标注难的动物行为分析场景，尤其助犬研究
助训犬的吠叫分类因样本稀少、个体差异大且伦理限制而难以开展。本文将其重构为二维唤醒-效价空间中的连续回归任务。核心方法是改进的孪生网络，不训练二元相似性，而是学习输入样本对之间的序数与数值距离。在公开数据集上训练后，模型在挑战性的效价维度上将周转率降低最高达50%，优于回归基线。在真实数据集上的定性验证表明，学习到的空间具有语义意义，验证了在严重数据限制下分析犬吠的可行性。
原文摘要 · Abstract (English)
Standard classification of canine vocalisations is severely limited for assistance dogs, where sample data is sparse and variable across dogs and where capture of the full range of bark types is ethically constrained. We reframe this problem as a continuous regression task within a two-dimensional arousal-valence space. Central to our approach is an adjusted Siamese Network trained not on binary similarity, but on the ordinal and numeric distance between input sample pairs. Trained on a public dataset, our model reduces Turn-around Percentage by up to 50% on the challenging valence dimension compared to a regression baseline. Qualitative validation on a real-world dataset confirms the learned space is semantically meaningful, establishing a proof-of-concept for analysing canine barking under severe data limitations.
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