Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/11355
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dc.date.accessioned2016-07-11T10:32:14Z-
dc.date.available2016-07-11T10:32:14Z-
dc.date.issued2015-
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/11355-
dc.descriptionB.SC.IT(HONS)en_GB
dc.description.abstractAs mobile technology becomes more sophisticated, its use to observe and survey the environment is becoming an integral part in the studies of natural ecosystems. Sensors required to perform certain fieldwork are now available within one compact and relatively inexpensive device. This study addresses the possibility of using mobile-technology to solve the problem of automatically recognizing bird species from the sound they make. Communication by sound plays a central role in the work of people who study birds. Unfortunately, recognizing birds only from their sound is not an easy task and a lot of training is required. In our research we managed to exploit the advantages of the Smart-Phone environment to create a proof of concept, which we named Tringa, that performs the full bird sound recognition lifecycle. The implementation of such a system was built using evolutionary prototyping, structured around the main stages of the process, namely: Sound Capture, Automatic Segmentation, Feature Extraction and Classification. Prior to the actual implementation, a thorough investigation of any related work and technology was conducted. This was mainly done so as the challenges brought by both the complexity of sound recognition and also by the limitations of Smart-Mobile device technology were not underestimated. One important obstacle to overcome was the varying quality of sound signals captured by the omni-directional microphones of smartphones in the uncontrolled recording environments in which birds are typically found. Such approach allowed us to perform a proper evaluation process of our implementation, which in turn revealed very encouraging and satisfactory results. This evaluation process was also key in determining which technology to use and which approaches to take. For the species set we chose for our study; segmenting recordings within the Energy-Time domain and using a k-Nearest Neighbour classifier trained with Mel-frequency Cepstral Coefficients provided the best results. Such work provides a solid foundation for future research in this area where mobile technology can help citizen science projects involving bird studies to increase their efficiency and reliability.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectSmartphonesen_GB
dc.subjectBirds -- Vocalizationen_GB
dc.subjectMobile computingen_GB
dc.subjectSignal processingen_GB
dc.titleInvestigating bird sound recognition for scientific surveying and citizen science on a smart phoneen_GB
dc.typebachelorThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holderen_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technologyen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorGalea, Nicholas-
Appears in Collections:Dissertations - FacICT - 2015

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