对生物声学呼叫标签光滑的传播变化模型不确定性
Georgios Rizos1, Jenna Lawson2, Simon Mitchell3
1GLAM - Group on Language, Audio, & Music, Department of Computing, Imperial College London, London SW7 2RH, UK.
Patterns (New York, N.Y.)
|March 15, 2024
概括
贝叶斯神经网络现在量化了预测不确定性,改善了野生动物呼叫检测. 无样本方法和不确定性意识的标签光滑提高了被动声学监测的准确性.
科学领域:
- 人工智能的人工智能
- 生物声学是一种生物声学.
- 机器学习 机器学习
背景情况:
- 贝叶斯神经网络 (BNNs) 与预测一起传播不确定性.
- 变量贝叶斯方法为不确定性估计提供无样本方法.
- 被动声学监测 (PAM) 对于野生动物研究至关重要.
研究的目的:
- 应用无样本的BNN方法来检测野生动物的呼叫,使用PAM数据.
- 引入不确定性意识的标签平滑,以改善模型培训.
- 在具有挑战性的生物声学数据集上评估拟议的方法.
主要方法:
- 利用了无样本变异贝叶斯神经网络.
- 开发了一种不确定性意识的标签光滑技术.
- 引入了来自马来西亚婆罗洲的新生物声学数据集,其中包括30种物种的重叠呼叫.
主要成果:
- 在AU-ROC中实现了1.5个百分点的绝对百分比改善.
- 在F1得分中表现出13分的改善.
- 预期校准误差 (ECE) 减少了19.5个点.
结论:
- 带有不确定性意识标签平滑的无样本BNN显著提高了野生动物呼叫检测准确度.
- 提出的方法为分析复杂的生物声学数据提供了强大的方法.
- 这项工作推动了人工智能在生态监测中的应用.
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