Fieldwork is a crucial component, involving collaborations with local researchers, conservationists, and indigenous communities to ensure sustainable and ethical research practices. We rigorously manage and analyze our data, employing both manual and automated techniques to review acoustic recordings. Recordings are meticulously organized and analyzed, incorporating additional environmental data logged to provide a comprehensive dataset. ​A standout feature of our approach is to create custom machine learning algorithm, capable of processing thousands of hours of audio much faster than manual methods. This technology will not only accelerate the research but also helps future-proof biodiversity by creating an extensive catalog of wildlife sounds, including those of species potentially threatened by climate change. Moreover, we are deeply invested in studying how weather influences wildlife vocalizations. By installing weather stations at each sampling site, we continuously monitor and record meteorological data, which is synchronized with our acoustic data. This integrated dataset allows us to explore correlations and visualize how changes in weather conditions, such as temperature and humidity, impact animal behavior and sound propagation. ​In conclusion, our project merges cutting-edge technology with traditional fieldwork to offer valuable insights into ecological dynamics and to assist in the conservation of natural soundscapes for future generations. Through sharing our findings in scientific publications and conferences, we aim to contribute to global understanding and preservation of biodiversity.