改进原型网络负例表示，提升少样本生物声学事件检测效果
Adaptive Learning via a Negative Selection Strategy for Few-Shot Bioacoustic Event Detection
- 设计自适应学习损失和负例选择策略，优化分类器更新
- 在DCASE 2023数据集上F-measure达0.703，提升12.84%
- 适合少样本生物声学识别任务，尤其目标发声时长变化大的场景
尽管原型网络（ProtoNet）在少样本生物事件检测中表现有效，但仍存在两个问题：一是缺乏显式标注的负样本，难以构建具有代表性的负原型；二是目标生物发声时长在不同任务间差异大，导致模型难以在所有任务上保持最优性能。为此，本文提出一种新的自适应学习框架，引入自适应学习损失以指导分类器更新，并设计负例选择策略，构建更具代表性的负原型。所有实验均在DCASE 2023 TASK5少样本生物声学事件检测数据集上进行。结果表明，所提方法在该数据集上的F-measure达到0.703，相比基线提升12.84%。
原文摘要 · Abstract (English)
Although the Prototypical Network (ProtoNet) has demonstrated effectiveness in few-shot biological event detection, two persistent issues remain. Firstly, there is difficulty in constructing a representative negative prototype due to the absence of explicitly annotated negative samples. Secondly, the durations of the target biological vocalisations vary across tasks, making it challenging for the model to consistently yield optimal results across all tasks. To address these issues, we propose a novel adaptive learning framework with an adaptive learning loss to guide classifier updates. Additionally, we propose a negative selection strategy to construct a more representative negative prototype for ProtoNet. All experiments ware performed on the DCASE 2023 TASK5 few-shot bioacoustic event detection dataset. The results show that our proposed method achieves an F-measure of 0.703, an improvement of 12.84%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。