English(EN) MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

MAST框架通过自训练增强生物多样性声音检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了MAST，一个用于生物多样性监测中高效鲁棒的声音检测的新框架。该方法结合了掩码音频预训练、轻量级检测器以及在无标签数据上的迭代自训练。MAST在识别不同环境下的动物叫声方面表现出显著的改进，包括印度尼西亚的热带雨林和西班牙的地中海鸟类栖息地，即使在分布发生变化的情况下也能有效识别。 AI

影响通过在标记数据有限的情况下进行更高效、更鲁棒的声音检测，提高了生物多样性监测能力。

排序理由该集群包含一篇详细介绍生物多样性监测声音检测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tianyi Xu, Daniel Pimentel-Alarc\'on, Zuzana Bu\v{r}ivalov\'a, Claudia Sol\'is-Lemus · 2026-09-15 04:00

MAST：通过掩码音频预训练和自训练实现标签高效、鲁棒且可泛化的声音检测，用于生物多样性监测

arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-specific, and difficult to sustain at scale. We present a label-efficient sound detec…

本文如何被排名

报道来源 [1]

MAST：通过掩码音频预训练和自训练实现标签高效、鲁棒且可泛化的声音检测，用于生物多样性监测

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