Acoustic Identication of Marine Cetaceans using Deep Learning Techniques

建立鲸目动物（海豚、鲸鱼和鼠海豚）动态和行为模型对于有效的种群管理和保护至关重要。设计和实施养护战略以及评估海上风力涡轮机和捕鱼等人为活动带来的风险需要可靠的数据。此外，鲸目动物是根据生态系统哨兵概念建立生态系统变化模型的主要候选者，因为它们反映了生态系统的现状，并对不同时空尺度的变化作出反应。随着全球气候变化和沿海地区城市化的加剧，必须制定方法，快速有效地评估海洋温度上升、污染和生境退化对生物和生态的影响。这可以通过建立海豚等大型海洋物种的种群、行为和健康的模型来实现，鲸目动物研究的方法包括被动声学监测。与其他海洋哺乳动物方法相比，部署的PAM成本较低，劳动密集型，可以记录长时间尺度的数据。对于海豚来说，这仅限于监测回声定位点击和使用它们产生的哨声识别某些物种，无法识别物种内的个体以及某些物种之间的差异（例如白吻海豚和白喙海豚）。该研究将通过将深度学习的方法和技术应用于海洋生物学领域来解决这些局限性，主要集中在图像分析技术上。将设计各种方法，以迅速查明个人身份、分析行为并采用遥感技术。根据这些鉴定，还将对群体规模进行估计。将制定一个统计框架，可纳入PAM系统，以监测物种和个体在长空间尺度上的发生情况。数据收集将集中在英格兰东北部海岸外的白喙海豚（WBD）种群。最近的研究已经确定了该物种经常出现的地点，并显示出季节性和多年的居住。一项健康评估发现，皮肤病和创伤的发病率很高，这表明保护这一人口应成为高度优先事项。此外，最近有证据表明，这种物种产生独特的哨声，但更多的证据是需要的，这项研究将提供。该研究将开发新的声学分析算法，可以部署在小型低成本的Linux为基础的计算机，被带到现场进行分析，在真实的时间。这些算法将被开发用于从WBD收集的可视化和原始声学数据，主要包括哨声。最初的数据收集过程已经开始，在英格兰东北部海岸探险期间，使用水听器记录海豚的叫声。声学记录的初步可视化也已经开始，但需要对水听器的深度和距离进行试验。该研究将开发一种新的方法和统计框架，使用深度学习模型进行声学分析。这一目标将通过测试训练算法在成功识别群体大小或个体从声学记录进行评估。研究的另一个目的是评估人类活动对鲸目动物的影响，主要是风力涡轮机和船只。布莱斯海岸附近有大量的风力涡轮机，那里是WBD和其他鲸目动物栖息地的集合。目前还没有研究这些涡轮机的噪音污染如何影响种群，但最近的研究表明，白喙海豚的创伤表明这可能是一个可能的原因。

Modelling cetacean (dolphins, whales and porpoises) dynamics and behaviour is paramount to effective population management and conservation. Robust data is required for the design and implementation of conservation strategies and to assess risk presented by anthropogenic (man-made) activity such as offshore wind turbines and fishing. Moreover, cetaceans make prime candidates for modelling ecosystem change under the ecosystem sentinel concept as they reflect the current state of the ecosystem and respond to change across different spatial and temporal scales. As global climate changes and urbanisation of coastal areas intensies, it is imperative to develop methodologies for quick and effective assessment of the biological and ecological impact of rising sea temperatures, pollution and habitat degradation. This can be achieved through modelling the population, behaviour and health of large marine species such as dolphins.Methodologies of cetacean research include passive acoustic monitoring (PAM). Deployed PAM is less expensive and labour intensive compared to other marine mammal methodologies and can record data over long temporal scales. For dolphins, this is limited to the monitoring of echolocation clicks and some species identification using the whistles they produce, unable to identify individuals within a species and difference between certain species (such as bottlenose and white-beaked dolphins). The research will addresses these limitations by applying the methodologies and techniques of deep learning to the field of marine biology, mainly focusing on image analysis techniques. Methodologies will be designed to quickly identify individuals, analyse behaviour and incorporate remote sensing techniques. From these identifications estimations of group size will also be made. A statistical framework will be developed, which can be incorporated into PAM systems, to monitor species and individual occurrence over long spatial scales.Data collection will focus on a population of white-beaked dolphins (WBD) off the coast of North-East England. Recent research has identified sites where the species is regularly sighted and has shown seasonal and multi-year residency. A health assessment identified high incidence of skin disease and trauma suggesting conservation of this population should be high priority. Moreover, there has been recent evidence of this species producing unique whistles, however more evidence is needed, which this research will provide.The research will develop novel acoustic analysis algorithms which can be deployed on small low-cost Linux based computers, to be taken into the field to perform analyses in real time. These algorithms will be developed to work on both visualised and raw acoustic data collected from WBD, consisting primarily of whistles. The process for initial data collection has already begun using hydrophones to record dolphin vocalisations during expeditions off the coast of the North-East England. Initial visualisation of acoustic recordings have also begun, but experimentation with depth and distance from vessel placement of hydrophones are needed. The research will develop a novel method and statistical framework using deep learning models for acoustic analysis. This objective will be evaluated by testing the trained algorithm at successfully identifying group size or individuals from acoustic recordings.Another aim of the research is to assess the effect of anthropogenic activity on cetaceans, mainly wind turbines and boats. There is a large collection of wind turbines off the coast of Blyth where the collection of WBD and other cetaceans habitat. There has been no studies of how the noise pollution from these turbines affect the population but recent research has shown trauma to the white-beaked dolphins suggesting this could be a possible cause.