Animal Sound

Animal sound analysis, or bioacoustics, aims to extract meaningful information from animal vocalizations for ecological monitoring and behavioral understanding. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and Bi-LSTMs), and increasingly, transformers, to classify, detect, and denoise animal sounds, often focusing on birdsong due to the availability of large datasets. These advancements enable more efficient and accurate species identification, habitat assessment, and even the analysis of animal interactions and emotional states, impacting conservation efforts and ecological research. Furthermore, research is actively developing methods for handling challenges like dialect variation and low signal-to-noise ratios, improving the robustness and generalizability of bioacoustic models.