Passive acoustic monitoring (PAM) enables large-scale biodiversity assessment, but continuous recording generates large amounts of non-informative audio, creating challenges for storage, power consumption, and long-term edge deployment. Bird audio detection (BAD), which identifies bird vocalizations, can reduce this burden by filtering irrelevant recordings before downstream analysis. However, most BAD systems are trained on temperate datasets despite tropical soundscapes being denser, more species-rich, and acoustically unpredictable. To address this gap, we introduce SEABAD (Southeast Asian Bird Activity Detection), a dataset of 50,000 curated three-second clips from Southeast Asian soundscapes, evenly balanced between bird-present and bird-absent samples. The dataset spans 1,677 bird species and is standardized to 16 kHz mono audio for embedded and low-power inference. We developed a dual-branch curation pipeline: a six-stage positive-label workflow applied to Xeno-Canto recordings, alongside six source-specific negative-label extractions from environmental datasets. These procedures reduced class imbalance by 13.7% (Gini coefficient: 0.601 to 0.519). A manual audit of 1,000 positive clips confirmed 97.8% +/- 0.9% labeling accuracy. Baseline experiments using MobileNetV3-Small achieved 99.57% +/- 0.25% accuracy and 0.9985 +/- 0.0002 AUC across three random seeds. SEABAD and the full curation pipeline are publicly released to support tropical BAD research and energy-efficient acoustic monitoring.

翻译：被动声学监测（PAM）能够实现大规模生物多样性评估，但连续录音会产生大量非信息性音频，给存储、功耗及长期边缘部署带来挑战。鸟类音频检测（BAD）通过识别鸟鸣声，可在下游分析前过滤无关录音以减轻这一负担。然而，尽管热带声景密度更高、物种更丰富且声学不可预测性更强，现有BAD系统多基于温带数据集训练。为解决这一差距，我们提出SEABAD（东南亚鸟类活动检测）——一个包含50,000个精选三秒片段的数据集，样本源自东南亚声景，其中鸟类存在与不存在样本均衡分布。该数据集涵盖1,677种鸟类，并标准化为16kHz单声道音频以适配嵌入式及低功耗推理。我们开发了双分支数据筛选流程：一条六阶段正标签处理流水线针对Xeno-Canto录音，另一条六类特定来源负标签提取流水线从环境数据集中获取样本。这些流程将类别不平衡度降低13.7%（基尼系数从0.601降至0.519）。对1,000个正标签片段的人工审核确认标注准确率为97.8%±0.9%。基于MobileNetV3-Small的基线实验在三个随机种子上取得99.57%±0.25%的准确率与0.9985±0.0002的AUC值。SEABAD数据集及完整筛选流程已公开，以支持热带BAD研究与能效型声学监测。