Audio-SAR-Dataset

Description The SAR-Dataset is a synthetic audio dataset designed for training and evaluating acoustic classifiers to detect human-made distress signals in post-disaster environments. This dataset supports research in drone-based search and rescue operations where acoustic detection can complement visual sensing systems. Dataset Generation The dataset is generated from isolated sound samples in the PostDisasterDataset [1] by combining human-made distress signals with realistic background noises typical of post-disaster environments (thunder, rain, engines, tools). Each human distress sound is overlaid with randomly selected background noise attenuated by 6 dB, resulting in mean SNR values between 10-20 dB across classes. Dataset Details Total samples: 15,000 ten-second audio recordings Classes (5): crying, coughing, screaming, knocking, none (3,000 samples each) Split: 80% training (12,000) / 10% validation (1,500) / 10% test (1,500) Audio format: 10-second mono recordings at 32 kHz SNR range: 10-20 dB mean File Naming Convention [UniqueID]_[HumanClassID]_[BackgroundClassID].wav Human Class ID 0 – coughing 1 – crying 2 – knocking 3 – screaming Background Class ID 0 – engine 1 – heavy equipment 2 – rain 3 – thunder 4 – tools 5 – water 6 – wind [1] Tuan-Anh Pham, Seong-Hun Park, Jong-Hoon Lee, Sang-Jin Han, Jungyu Choi, and Sungbin Im. An audio dataset and hierarchical taxonomy for post-disaster scenes. In Proceedings of Acoustics 2024. Australian Acoustical Society, November 2024.