Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set.
Proc Natl Acad Sci U S A
; 117(29): 17049-17055, 2020 07 21.
Article
en En
| MEDLINE
| ID: mdl-32636258
ABSTRACT
Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Espectrografía del Sonido
/
Acústica
/
Monitoreo del Ambiente
/
Ecosistema
/
Aprendizaje Automático
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2020
Tipo del documento:
Article