Mitigating Domain Shift in Bioacoustics: Adapting Foundation Models for Passive Acoustic Monitoring
Foundation models have revolutionized image analysis, achieving remarkable results in tasks like object detection and classification through large-scale pretraining. Their adaptation to audio analysis shows promise, as seen in preliminary efforts with models like AudioCLIP, Wav2CLIP, and CLAP, which link audio to natural language representations. However, these models face significant challenges when applied to passive acoustic monitoring, where domain shifts—caused by variations in species distribution, noise conditions, and differences in recording equipment—significantly affect their performance, especially in detailed species classification. Passive acoustic monitoring is crucial for biodiversity conservation as it provides valuable insights into animal behavior, environmental impacts, and ecosystem management. Nevertheless, current AI-based methods often struggle to adapt to new sampling conditions or regional differences, limiting their effectiveness. Traditional models, such as convolutional neural networks (CNNs), though commonly used, often fail to handle domain shifts. This research proposes an approach that integrates foundation models into passive acoustic monitoring to enhance adaptability and reduce domain adaptation issues. We aim to improve the robustness of species classification models against domain shifts, ensuring more reliable monitoring of animal populations. This approach not only addresses the limitations of existing models but also advances the state of AI applications in biodiversity conservation, offering a methodology to more efficient and adaptable methods for analyzing biodiversity data in changing and diverse environments.