Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Passive acoustic monitoring (PAM) has emerged as a transformative tool for applied
ecology, conservation and biodiversity monitoring, but its potential contribution to …

High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of
global change and biodiversity declines. While real‐time and automated monitoring of …

Automated bioacoustic analysis aids understanding and protection of both marine and
terrestrial animals and their habitats across extensive spatiotemporal scales, and typically …

Abstract Machine learning covers a large set of algorithms that can be trained to identify
patterns in data. Thanks to the increase in the amount of data and computing power …

Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast
scales, existing analytical approaches behave unpredictably across studies. We collated …

Computational ecoacoustics has seen significant growth in recent decades, facilitated by the
reduced costs of digital sound recording devices and data storage. This progress has …

Tracking biodiversity and its dynamics at scale is essential if we are to solve global
environmental challenges. Detecting animal vocalizations in passively recorded audio data …

The efficient analyses of sound recordings obtained through passive acoustic monitoring
(PAM) might be challenging owing to the vast amount of data collected using such …

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can
complement or serve as an alternative to real‐time visual or aural monitoring of marine …