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 …

In this study, AlexNet with transfer learning was employed to automatically detect and
classify the sounds of killer whales, long-finned pilot whales, and harp seals with widely …

Deep clustering was applied to unlabeled, automatically detected signals in a coral reef
soundscape to distinguish fish pulse calls from segments of whale song. Deep embedded …

Fish spawning aggregations (FSAs) consist of the temporary gathering of a large number of
fishes at a specific location to spawn. Monitoring of FSA is typically conducted by divers, but …

Many commercially important groupers (Epinephelidae) form fish spawning aggregations
(FSA) at specific sites where the spawning stock is concentrated to spawn within a couple of …

In the fields of technology such as aerospace, anomaly detection is critical to the overall
system. With the large increase in data volume and dimensions, the traditional detection …

In this paper, we present the first machine learning package developed specifically for fish
calls identification within a specific range (0–500Hz) that encompasses four Caribbean …

Ocean noise has a negative impact on the acoustic recordings of odontocetes' echolocation
clicks. In this study, deep convolutional autoencoders (DCAEs) are presented to denoise the …

Ocean noise negatively influences the recording of odontocete echolocation clicks. In this
study, a hybrid model based on the convolutional neural network (CNN) and long short-term …

This study is a systematic review of literature on the classification of sounds in three domains-
Bioacoustics, Biomedical acoustics, and Ecoacoustics. Specifically, 68 conferences and …