The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

3D audio techniques for acoustic monitoring of rainforest biodiversity
Sound carries substantial information about local biodiversity, being used for navigation and communication by a wide range of taxa. Acoustic information is now commonly used to assist in point-surveys of many of these species, aiding the identification of bats, grasshoppers, birds, amphibians, and even individual animals. The integration of 3D audio sensors for acoustic monitoring could offer additional cues in order not only to be able to identify the species and numbers, but also to gather information about movements, direction, velocity, and other relevant data. A 3D microphone array will be designed and integrated within a custom built multichannel audio recorder. High-quality low-power electret microphones will be arranged in a spherical array, in order to allow the recording of omnidirectional audio cues, as well as directional ones. The whole system will be integrated with a solar-powered deep-cycle battery, GPRS/3G/4G connectivity for regularly uploading recorded data, and data compression capabilities. The system will designed in order to allow for simple one-manned installation on a tree. A series of benchmark tests will be carried out in order to calibrate the recording quality and data compression, allowing the recording of meaningful and usable data, and at the same time giving the possibility or regularly upload the recording data with non-optimal network coverage. The system will be initially tested in the UK, using the VR acoustic facilities within the Dyson School of Design Engineering and the field sites at Silwood Park, and will then be deployed in Malaysia, as part of the on-going monitoring by Prof Ewers’ research group. With the recorded data, previously developed statistical methods will be used to implement autonomous, continuous biodiversity monitoring. First, species calls in the acoustic signal will be automatically detected using existing signal processing techniques, which allow to apply a battery of more than 9000 signal processing algorithms to species’ calls to develop an acoustic ‘fingerprint’ for species (support from Dr Nick Jones from Imperial Mathematics will be sought for this specific research stage). Second, the spatial audio data will be decoded using techniques such as beamforming and 3D Ambisonic, to determine angular position and distance of detected sounds and calls. Third, we will use these data to identify individuals within an acoustic record, and use those data to apply detectability statistics to gain more accurate measures of species’ abundances. These metrics will be calibrated using field data on the species richness of bats, birds, amphibians, mammals and invertebrates, collected as part of on-going monitoring by Prof Ewers’ research group in Malaysia. Snapshot samples of diversity of the various taxa at each of the acoustic monitoring sites will be used at multiple time points to test the correlations between observed biodiversity and acoustically derived species records and information metrics. These calibrated metrics will be compared in primary and logged rainforest and along a gradient of historical logging intensity to determine the impacts of forest disturbance on biodiversity. The proposed research builds on a current PhD project (supervised by the same team), and aims at integrating on the developed acoustic monitoring device 3D microphone arrays and encoders, and at implementing novel methods and techniques to decode and analyse the spatial acoustic data.
Lorenzo Picinali
Rob Ewers
Computing, Quantitative data analysis, Ecological observations / data collection
Rob Ewers
The student will acquire quantitative skills in a broad range of NERC Critical Skills Gaps: (1) Data Management - Specific Needs ‘Interrogating large datasets and data mining’ and ‘Environmental informatics’; (2) Numeracy - Specific Need ‘Statistical methods for handling, analysing and interpreting large datasets; and (3) Taxonomy and Systematics - Specific Need ‘Biological Monitoring’.
Within this project, novel computational tools (both hardware and software) for using acoustic data for biodiversity monitoring will be developed. The use of 3D audio information will allow for more precise recognition of species and for gathering additional data about the location and movements.
By comparing observed species’ abundances to acoustically recorded species’ abundances across a land use gradient, we will be able to test the theory that human modification of forests changes the acoustic behaviour of species. We expect to find species in modified habitats vocalizing less often and at higher frequencies than in primary habitats.
Acoustic monitoring systems allow for continuous high-throughput remote data collection, assimilation and analysis. The developed tool (both HW and SW) will be released to the research community as a cost-effective remote acoustic monitoring system, allowing the collection and analysis of very large amounts of quantitative data about local biodiversity.
Many research groups already use acoustic recording to monitor biodiversity, but those methods do not allow for the identification and tracking of individual animals. Developing this new technology will allow modern detectability statistics to be applied to acoustic monitoring systems, providing more accurate measures of species’ abundances.
This project encompasses electroacoustics, computing and ecology. Dr Picinali will oversee the electroacoustic engineering, as well as the 3D audio recording, decoding and analysis, and the computing side. Prof Ewers will oversee the ecological aspects, including the application of detectability statistics to acoustic data, which form the basic questions the project is setting out to address.
Behavioural ecology, Conservation ecology, Ecological/Evolutionary tools, technology & methods
The student will receive specific training in the areas of acoustics, DSP and audio technology within the Dyson School of Design Engineering. Additional training in Ecology will be offered within the Department of Life Sciences by Prof Ewers team. Dr Nick Jones’ team from Imperial Mathematics will give support for the implementation of efficient algorithms for the processing of the acoustic data.
Dyson School of Design Engineering (South Kensington) and Silwood Park
2017-09-29 12:45:02