SEABAD: Southeast Asian Bird Activity Detection Dataset

SEABAD (Southeast Asian Bird Activity Detection) is a large-scale audio dataset for Edge AI bird detection, comprising 25,000 positive (bird-present) and 25,000 negative (bird-absent) 3-second WAV clips sampled at 16 kHz. The dataset represents all available bird sound samples from Xeno-canto recordings in Malaysia and surrounding countries up to November 2025. The dataset is designed for automated bird activity detection, biodiversity monitoring, and real-time acoustic sensing on low-power devices, and is fully machine-learning–ready. Positive samples were extracted from Xeno-canto recordings of Malaysian bird species by selecting the highest-energy 3-second segment from each source recording. Negative samples originate mainly from the BirdCLEF “BAD” competition (BirdVox, Freefield1010, Warblr) and are supplemented with non-bird environmental audio from ESC50, FSC22 and DataSEC datasets. Released under CC BY 4.0, MyBAD provides an open, reproducible resource for developing Edge AI models for tropical bird monitoring and acoustic biodiversity assessment. Note: This dataset will be formally described in a forthcoming paper introducing the dataset and companion featherweight bird-activity detector.