Computational bioacoustics with deep learning: a review and roadmap

TL;DR

This paper reviews the current state of deep learning in computational bioacoustics, highlighting challenges, knowledge gaps, and proposing a roadmap for future research to advance ecological and zoological insights.

Contribution

It provides a comprehensive review of deep learning applications in bioacoustics and offers a strategic roadmap for future research directions in the field.

Findings

Deep learning methods are increasingly applied to bioacoustic data.

There are significant knowledge gaps and unsolved problems in the field.

A roadmap is proposed to guide future research in computational bioacoustics.

Abstract

Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.