Applications of machine learning to identify and characterize the sounds produced by fish

Abstract

Abstract Aquatic ecosystems are constantly changing due to anthropic stressors, which can lead to biodiversity loss. Ocean sound is considered an essential ocean variable, with the potential to improve our understanding of its impact on marine life. Fish produce a variety of sounds and their choruses often dominate underwater soundscapes. These sounds have been used to assess communication, behaviour, spawning location, and biodiversity. Artificial intelligence can provide a robust solution to detect and classify fish sounds. However, the main challenge in applying artificial intelligence to recognize fish sounds is the lack of validated sound data for individual species. This review provides an overview of recent publications on the use of machine learning, including deep learning, for fish sound detection, classification, and identification. Key challenges and limitations are discussed, and some points to guide future studies are also provided.