Automatic acoustic identification of individual animals in the wild

自动声学识别野外个体动物

• 批准号: 2374972
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2374972
• 关键词: Automatic; acoustic; identification; individual; animals

Automatic acoustic identification of individuals in the wild concerns the development of machine learning methods that can identify specific individuals based on their vocalizations. Furthermore, the developed methods should be able to operate across a multitude of species and environments that mimic the unrestrained conditions of the real-world.From the zoology field it has been shown how several species are able to produce vocalizations containing distinct characteristics that inform about the individual, these characteristics, are often referred to as acoustic signatures.Given the variety of species (from across all taxa) that use acoustic signatures and especially the variety of vocal systems or the different communication strategies animals employ, it is expected that these signatures won't manifest in a simple consistent way, that could be detected by standard signal processing. This is the main reason why automatic systems for acoustic identification of individuals are seldom developed to work with different species, and are instead highlyspecialized in the identification of individuals within a single species and often within a small group of individuals.An important point of this topic is to address the machine learning challenges that the application of such a system in a real-world scenario bring. Not only being able to work with different species but also being able to cope with varying environment conditions, different habitats etc. In the wild we cannot impose hard boundaries regarding the species that we are going to record, the type of calls, or the individuals that will enter the targeted space. Ideally, we want our system to work equally well across all these different conditions, but it is not desirable to fine tune the model or define specific instructions for every potential situationIn recent years, deep learning methods have been applied very successfully on several bioacoustic related tasks. Especially, multi-task learning is an interesting methodology that contributes to the generalisation capabilities of the models and thus an interesting approach to be explored here.

野外个体的自动声学识别涉及机器学习方法的发展，该方法可以根据发声来识别特定个体。此外，所开发的方法应该能够在模仿现实世界不受限制的条件的多种物种和环境中运行。从动物学领域来看，已经表明一些物种如何能够发出包含不同特征的发声，这些特征表明个体的这些特征通常被称为声学特征。考虑到使用声学特征的物种（来自所有分类单元）的多样性，特别是声音系统的多样性或动物采用的不同交流策略，预计这些特征不会体现在一种简单一致的方式，可以通过标准信号处理来检测。这就是为什么用于个体声学识别的自动系统很少被开发来与不同物种一起工作，而是高度专业化地识别单个物种内的个体，并且通常是一小群个体内的个体。本主题的一个要点是为了解决这种系统在现实场景中的应用所带来的机器学习挑战。不仅能够与不同的物种合作，而且能够应对不同的环境条件、不同的栖息地等。在野外，我们不能对我们要记录的物种、叫声类型或个体施加严格的界限将进入目标空间。理想情况下，我们希望我们的系统在所有这些不同的条件下都能同样良好地工作，但不希望针对每种潜在情况对模型进行微调或定义特定指令近年来，深度学习方法已非常成功地应用于几个生物声学相关的任务。特别是，多任务学习是一种有趣的方法，有助于提高模型的泛化能力，因此是这里要探索的有趣方法。