Short-time acoustic indices for monitoring urban-natural environments using artificial neural networks

Abstract

Urban-natural environments, proximal to rapidly urbanizing cities, provide essential ecosystem functions that benefit both city residents and ecological communities. With escalating urbanization, the resilience of these ecosystems is being progressively challenged, highlighting the need for robust monitoring mechanisms. Acoustic monitoring has emerged as an unobtrusive method to evaluate the status of these environments, capitalizing on indicators that reflect both landscape features and specific acoustic events. Despite potentially offering significant insights, this approach generates a large volume of acoustic data, introducing complexities in subsequent analyses. To mitigate this, we propose integrating artificial neural networks with acoustic indices to enhance data analysis. Our approach emphasizes the usefulness of short-time acoustic indices, computed over finite-duration analysis windows, to enhance polyphonic sound event detection accuracy. Empirical results support the performance of our approach, registering both an F1-Score and an error rate of 0.614. Overall, this study delineates a novel paradigm geared towards enhancing or preserving the biological diversity of urban-natural environments in areas with population growth and urban development.

Keywords

• Passive acoustic monitoring
• Machine learning
• Soundscape
• Acoustic Indices
• Feature Extraction