
Audio Localisation
This audio localisation method aims to provide a complete pipeline from Passive Acoustic Monitoring to acoustic source localisation by automatically identifying and triangulating vocalisation events from arrays of microphones with overlapping range.
This audio localisation method is a sophisticated approach that delves into the realm of Passive Acoustic Monitoring (PAM) with the ultimate goal of achieving precise acoustic source localisation. The method is designed to streamline the process of identifying and triangulating vocalisation events using arrays of microphones strategically placed with overlapping ranges. By leveraging densely clustered microphones with 100m spacings, the methodology aims to capture a comprehensive soundscape of a specific valley under study.
Through the meticulous spatial mapping of sound sources, the methodology seeks to generate high-resolution spatial data that can offer a detailed representation of the acoustic environment within the valley. This data, when coupled with advanced species identification techniques, holds the potential to unlock valuable insights into the intricate dynamics of the ecosystem at a fine-scale level.
One of the key advantages of this approach lies in its ability to provide researchers with a nuanced understanding of activities such as feeding and trapping within the ecosystem. By combining the spatial information with species-specific vocalisations, the method opens up avenues for researchers to explore the interactions between different species, their behaviors, and the ecological processes that govern their existence.
Overall, this method represents a cutting-edge tool that not only facilitates the localization of acoustic sources but also offers a pathway towards a deeper comprehension of the ecological intricacies present within the study area. Through its innovative design and integration of advanced technologies, this approach stands poised to contribute significantly to the field of soundscape ecology and ecosystem dynamics.
![]() | Kaspar Soltero, PhD Candidate, Listening Lab |