![]() ![]() The revolution in this field is real, but it is recent: reviews and textbooks as recently as 2017 did not give much emphasis to DL as a tool, even when focussing on machine learning for bioacoustics ( Ganchev, 2017 Stowell, 2018). The resulting deluge of audio data means that a common bottleneck is the lack of person-time for trained analysts, heightening the importance of methods that can automate large parts of the workflow, such as machine learning. This is both enabled and demanded by the twenty-first century data deluge: digital recording devices, data storage and sharing have become dramatically more widely available, and affordable for large-scale bioacoustic monitoring, including continuous audio capture ( Ranft, 2004 Roe et al., 2021 Webster & Budney, 2017 Roch et al., 2017). This includes audio domains such as automatic speech recognition and music informatics ( Abeßer, 2020 Manilow, Seetharman & Salamon, 2020).Ĭomputational bioacoustics is now also benefiting from the power of DL to solve and automate problems that were previously considered intractable. ![]() Within machine learning, deep learning (DL) has recently revolutionised many computational disciplines: early innovations, motivated by the general aims of artificial intelligence (AI) and developed for image or text processing, have cascaded through to many other fields ( LeCun, Bengio & Hinton, 2015 Goodfellow, Bengio & Courville, 2016). Bioacoustics has long benefited from computational analysis methods including signal processing, data mining and machine learning ( Towsey et al., 2012 Ganchev, 2017). 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.īioacoustics-the study of animal sound-offers a fascinating window into animal behaviour, and also a valuable evidence source for monitoring biodiversity ( Marler & Slabbekoorn, 2004 Laiolo, 2010 Marques et al., 2012 Brown & Riede, 2017). 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. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. ![]() Methods are inherited from the wider field of deep learning, including speech and image processing. 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. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. ![]()
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