• Shruti Shinde
• Kartik Mani

As biodiversity conservation gains paramount importance, the need for efficient wildlife monitoring techniques becomes crucial. This project presents an innovative approach to wildlife monitoring through the application of artificial intelligence (AI) to analyse wildlife audio data. The system leverages advanced machine learning algorithms for audio recognition, enabling the identification of various species based on their unique vocalizations. The project’s key components include a robust audio data collection system, pre-processing techniques to enhance signal quality, and a machine learning model trained on a diverse data set of wildlife sounds. The AI model employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to recognize and classify different animal calls. The application of this AI-based wildlife audio monitoring system extends beyond species identification; it includes behavioural analysis, population estimation, and habitat health assessment. The system’s real-time processing capabilities allow for instantaneous feedback, aiding wildlife researchers, conservationists, and environmentalists in making informed decisions for habitat management and preservation. The project contributes to the advancement of wildlife monitoring technologies, providing a scalable and cost-effective solution for ecologists and researchers working towards the conservation of diverse ecosystems.

Wildlife, Acoustic, AI, Monitoring, Audio, Recognition, Sentinel, Ecology, Conservation, Surveillance

"Jungle-Echo: AI-Based Audio Monitoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.b232-b241, January-2024, Available :http://www.jetir.org/papers/JETIR2401126.pdf

"Jungle-Echo: AI-Based Audio Monitoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppb232-b241, January-2024, Available at : http://www.jetir.org/papers/JETIR2401126.pdf