2026-10-14T23:11:40Z https://www.scidb.cn/oai

10.57760/sciencedb.zoology.00013 2026-07-14T11:11:43Z

2026-07-14 Building High-quality Corpus for Sound of Wild Animals doi:10.57760/sciencedb.zoology.00013 en Passive acoustic monitoring is a critical technique for biodiversity surveys, wildlife conservation, and ecological research. With rapid advances in acoustic sensing technologies and data analysis algorithms, acoustic monitoring is transitioning from traditional ecological surveys to an AI-driven stage. Despite the exponential growth of animal acoustic data, high-quality, uniformly annotated and verified datasets compatible with AI model training remain scarce. Here we present a comprehensive methodology for constructing a wildlife sound dataset tailored to AI model development, covering multi-source heterogeneous data collection, standardised preprocessing pipelines, a quality control system, and a quantitative evaluation framework. The current release covers three taxonomic groups: Aves, Amphibia, and Insecta, comprising 44 species, 108 audio recordings with matching annotation files, 1,036 annotated bounding boxes, and 1,036 spectrogram images. This addresses the shortage of high-quality, fine-grained acoustic samples for native Chinese wildlife. We also provide a reproducible and scalable framework for acoustic data processing and quality control to support AI-based acoustic identification and biodiversity monitoring. The dataset is archived and publicly accessible, offering a vital resource for zoological bioacoustics research and wildlife conservation. Acoustic dataset; Acoustic signals; Wildlife; Annotation; AI Shaolian Chen Gang Song Wenjie Li Xiaoqing Ding Yabin Ding Ting Wang Shakhzod I. Dekhkonov Bo Cai Weibing Zhu Congtian Lin PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank