用AI分析声音数据，实时监测物种分布与生态健康。
AI-Enhanced Acoustic Analysis for Comprehensive Biodiversity Monitoring and Assessment
- 部署声学传感器网络，结合AI识别物种叫声
- 可区分自然声与人为噪声，准确追踪物种变化
- 适合生态研究者与环保政策制定者使用
本项目提出构建一个基于声学传感器网络与先进人工智能算法的综合性实时生物多样性监测系统。该系统通过分析不同生态系统的音频数据，识别并分类物种，为生态系统健康与生物多样性格局提供洞察，并支持对物种存在与行为细微变化的长期监测。针对噪声污染与物种重叠等关键挑战，系统采用先进的滤波与分类技术，确保监测结果的准确性与可靠性，能够有效区分自然声音与人为干扰声。最终，该系统旨在深化对生物多样性动态的理解，为制定有效的保护策略和政策决策提供关键信息，使利益相关方获得可操作的生态洞察，以保护和维护重要生态系统。
原文摘要 · Abstract (English)
This project proposes the development of a comprehensive real-time biodiversity monitoring system that harnesses sound data through a network of acoustic sensors and advanced artificial intelligence algorithms. The system analyzes sound recordings from various ecosystems to identify and classify different species, providing valuable insights into ecosystem health and biodiversity patterns while facilitating the detection of subtle changes in species presence and behavior over time. By addressing critical challenges such as noise pollution and species overlap, the system employs sophisticated filtering and classification techniques to ensure accurate and reliable monitoring, distinguishing between natural sounds and anthropogenic noise. Ultimately, this initiative aims to enhance our understanding of biodiversity dynamics and provide essential information to support effective conservation strategies and inform policy decisions, empowering stakeholders with actionable insights to protect and preserve vital ecosystems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。