Assessing a domain-adaptive deployment workflow for selective audio recording in wildlife acoustic monitoring

Abstract

Passive acoustic monitoring is a valuable tool for wildlife research, but scheduled recording often results in large volumes of audio, much of which may not be of interest. Selective audio recording, where audio is only saved when relevant activity is detected, offers an effective alternative. In this work, we leverage a low-cost embedded system that implements selective recording using an on-device classification model and evaluate its deployment for detecting penguin vocalizations. To address the domain shift between training and deployment conditions (e.g., environment, recording device), we propose a lightweight domain adaptation strategy based on fine-tuning the model with a small amount of location-specific data. We replicate realistic deployment scenarios using data from two geographically distinct locations, Antarctica and Falkland Islands, and assess the impact of fine-tuning on classification and selective recording performance. Our results show that fine-tuning with location-specific data substantially improves generalization ability and reduces both false positives and false negatives in selective recording. These findings highlight the value of integrating model fine-tuning into field monitoring workflows, in order to improve the reliability of acoustic data collection.