Semi-supervised classification of bird vocalizations

Changes in bird populations can indicate broader changes in ecosystems, making birds one of the most important animal groups to monitor. Combining machine learning and passive acoustics enables continuous monitoring over extended periods without direct human involvement. However, most existing techniques require extensive expert-labeled datasets for training and cannot easily detect time-overlapping calls in busy soundscapes. We propose a semi-supervised acoustic bird detector designed to allow both the detection of time-overlapping calls (when separated in frequency) and the use of few labeled training samples. The classifier is trained and evaluated on a combination of community-recorded open-source data and long-duration soundscape recordings from Singapore. It outperforms the state-of-the-art BirdNET classifier on a test set of 103 bird species despite significantly fewer labeled training samples. The detector is further tested on 144 microphone-hours of continuous soundscape data. The rich soundscape in Singapore makes suppression of false positives a challenge on raw, continuous data streams. Nevertheless, we demonstrate that achieving high precision in such environments with minimal labeled training data is possible. Introduction Biodiversity monitoring is a critical aspect of biodiversity conservation, as it helps inform decision making, improves our knowledge and enhances public education and awareness. Birds are one of the most surveyed animal groups in biodiversity monitoring programmes, with point counts and transect surveys being well-established survey techniques for monitoring bird communities [1]. However, birds can be very difficult to detect and identify especially in tropical regions characterised by high avian diversity and numerous rare species [2], [3]. Additionally, such manned survey techniques are manpower-intensive, require highly specialized expertise, and tend to overlook rare species that are sensitive to human presence [4], [5], [6]. Passive monitoring of biodiversity using acoustics is thus an area of great potential, as various animal groups including birds make unique vocalizations, which can be used to validate their presence.