Soundscape Identification of Endangered Bird Species using Bidirectional Long Short Term Memory

In this paper, bird vocalization, a key indicator for species identification, is a valuable tool for studying avian population, especially for endangered species whose population is difficult to monitor through traditional methods. In this paper presents an automated system dedicated to identify the endangered bird species through the analysis of bird vocalizations using a Bidirectional Long Short-Term Memory model. As compared to traditional approaches, the BiLSTM architecture is particularly apt for this task since it can learn audio data patterns by processing information in both forward and backward temporal directions, enhancing classification accuracy in scenarios where sound patterns may vary. This proposed system also aims to provide a vital tool for conservationists, ecologists, and researchers, offering a scalable solution for tracking avian population trends.