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.
翻译:动物声学和自然声学是令人着迷的研究对象,含有关于动物行为、人口和生态系统的宝贵证据。它们都是在生物学和生态学中研究的,信号处理和分析是一个重要的组成部分。近几十年来,由于廉价数字声学记录装置的发展,以及诸如大数据、信号处理和机器学习等信息学的巨大进步,计算生物学加速了近几十年来生物学学的步伐。方法来自深层学习的更广泛领域,包括言语和图像处理。然而,任务、要求和数据特征往往不同于语言或音乐分析中处理的问题、要求和数据特征。还存在尚未解决的问题和任务,证据肯定在许多声学信号中出现,但尚未实现。在本文中,我对计算生物学深层学习的艺术状况进行了审查,目的是澄清关键概念,查明和分析知识差距。在此基础上,我为深层学习的计算生物学提供了主观但有原则性的路线图:社区应该处理的专题,以便尽量掌握AI和信息学的未来发展,并在生态学中使用音学数据。