Acoustic Identication of Marine Cetaceans using Deep Learning Techniques

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.

The CDT will have impact in a range of areas:


Industrial and Public Sector Impact

The Centre's main impact will be made through its graduates: it will develop highly skilled researchers with the theoretical and practical skills to transform existing organisations, and create successful new companies.

We have already obtained commitment from 30 partner organisations both large and small, regional, national and international, who wish to work closely with the CDT (as evidenced by the letters of support). Impact on them will come through students working on projects specified by partners, students being placed with partners during their PhD, and ultimately through students moving into positions of influence in organisations when they graduate.
The norm for all software developed in the CDT will be to release it as open source so that it can be exploited by industry. In our experience this can attract companies and be a catalyst for productive collaboration - code from our previous projects has been widely used internationally.


Economic Impact

The global cloud computing market is expected to grow from $38 billion in 2010 to $121 billion in 2015 (M&M, 2013). Working productively with partners will maximise the chances of economic impact, which will come through organisations using their newfound skills, expertise and tools to realise their potential to transform themselves.

UK industry faces a huge skills gap in this area. Demand for big data staff has risen exponentially (912%) over the past five years from 400 advertised vacancies in 2007 to almost 4,000 in 2012 (e-skills UK, Jan 2013). Over the next five years analysts forecast a 92% rise in the demand for big data skills with around 132K new jobs being created in the UK (e-skills UK, Jan 2013). The CDT will provide expert practitioners to fill this gap.

The reason Newcastle City Council is setting up the £2M cloud business engagement facility that will be co-located with the CDT is that it believes that it can transform the local economy by up-skilling existing workers. This investment brings funding for CPD, cloud events and other outreach activities that will disseminate the knowledge developed in the CDT.


Societal Impact

We will build on the knowledge and pathways created in the Social Inclusion through the Digital Economy Hub (SiDE: 2009-15), which is tackling big data challenges across a range of areas of societal importance e.g. healthcare and mobility for older people. We will build on our existing, long-term relationships with SiDE partners; maintain our links with organisations that represent disadvantaged groups; and work directly with users through the 3000 person User Pool created by the SiDE project.

The CDT also has a strong set of investigators tackling key healthcare challenges through the use of cloud computing in medicine, biology and neuroscience. These subjects are now under a deluge of data, and increasingly researchers (including those in the pool of potential supervisors for this CDT) are using cloud computing to extract knowledge from it.

An annual public engagement open-day will disseminate the CDT's work to a diverse audience.


Academic Impact

Academic impact will come from the graduating students (some of whom will stay in academia), ideas (through publications), the publication of open source software and our delivery of training courses to other CDTs and researchers.

The placing of CDT students at our overseas partner Universities - Berkeley and PUCRS, Brazil (please see letters of support) - will provide a way for our student's research to have direct international impact.