A Citizen Science Based Approach to Automated Taxon Identification Using Computational Bioacoustics and Unsupervised Feature Learning

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Context:The research project will investigate novel approaches to the design of algorithms and methods for automated taxon identification (ATI), i.e. the automated identification of species. The application areas for ATI are numerous, ranging from rapid assessment of biodiversity and monitoring of habitat health and changes to early identification of agricultural pests and overcoming the taxonomic impediment (addressing the lack of trained taxonomists). The sensors for ATI system can include images, bioacoustic signals, radar and sonar. This project will concentrate on bioacoustic signals such as singing insects, birds and possibly mammals. Development of a successful robust and scalable ATI system will have significant impact in fields such as biodiversity assessment, habitat quality monitoring, environmental planning and citizen science.Aims and Objectives:a) To develop spectral and temporal feature extraction methods for a number of bioacoustically active taxa.b) To investigate novel AI techniques for optimal separation of taxa using extracted feature sets. This will include combining artificial neural networks with expert systems for enhanced and flexible recognition capability. The systems must be capable of operating in natural environments with high levels of acoustic interference.c) To evaluate the methods developed for singing insects including cicadas and Orthoptera (grasshoppers and crickets).d) To implement successful algorithms on smartphones to create and test citizen science oriented approaches to species identification.Novelty:Novel aspects of the project are in several areas: (i) new feature sets for time varying signals, e.g. multiscale time domain signal coding (MTDSC); (ii) combining more traditional artificial neural networks (MLP, recurrent nets) with expert systems to encapsulate non-parametric data such as biogeographical information and phenology; (iii) develop scalable architectures; (iv) application to smart phones for citizen science based applications.Alignment with EPSRC Research Areas:Artificial Intelligence Technologies - the project will investigate the use of unsupervised machine learning techniques combined with expert system approaches to create scalable and robust bioacoustic signal recognition.Digital Signal Processing - the success of the project will strongly rely on the extraction of features from the bioacoustic signals, this will require DSP techniques.Music and Acoustic Technology - the project will concentrate on acoustic signals of biological origin, their analysis and identification.Companies and Collaborators Involved:There are no companies involved; we have access to field sites via Forestry Commission, Wildlife Trusts and Natural England. We hope to engender more collaboration as the project progresses.